Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition
Abbas Ghaddar, Philippe Langlais†, Ahmad Rashid and Mehdi Rezagholizadeh Huawei Noah’s Ark Lab, Montreal Research Center, Canada †RALI/DIRO, Université de Montréal, Canada [email protected], [email protected] [email protected], [email protected]
Abstract Gonzales, Louisiana LOC In this work, we examine the ability of NER GonzalesPER is a small city in Ascension models to use contextual information when Parish, Louisiana. predicting the type of an ambiguous entity. Obama, Fukui We introduce NRB, a new testbed carefully LOC designed to diagnose Name Regularity Bias ObamaPER is located in far southwestern of NER models. Our results indicate that all Fukui Prefecture. state-of-the-art models we tested show such Patricia A. Madrid a bias; BERT fine-tuned models signifi- MadridPER won her first campaign in 1978 .. cantly outperforming feature-based (LSTM- LOC CRF) ones on NRB, despite having com- Asda Jayanama parable (sometimes lower) performances on PER standard benchmarks. AsdaORG joined his brother, Surapong ... To mitigate this bias, we propose a novel model-agnostic training method which Figure 1: Examples extracted from Wikipedia (ti- adds learnable adversarial noise to some tle in bold) that illustrate name regularity bias entity mentions, thus enforcing models to focus more strongly on the contextual in NER. Entities of interest are underlined, gold signal, leading to significant gains on types are in blue superscript, model predictions are NRB. Combining it with two other training in red subscript, and context information is high- strategies, data augmentation and parameter lighted in purple. Models employed in this study freezing, leads to further gains. disregard contextual information and rely instead on some signal from the named-entity itself. 1 Introduction Recent advances in language model pre- Agarwal et al., 2020b; Zeng et al., 2020) in NER training (Peters et al., 2018; Devlin et al., occurs when a model relies on a signal coming 2019; Liu et al., 2019) have greatly improved from the entity name, and disregards evidences the performance of many Natural Language within the local context. Figure 1 shows examples Understanding (NLU) tasks. Yet, several stud- where state-of-the-art models (Peters et al., 2018; ies (McCoy et al., 2019; Clark et al., 2019; Utama Akbik et al., 2018; Devlin et al., 2019) fail to ex- arXiv:2107.11610v1 [cs.CL] 24 Jul 2021 et al., 2020b) revealed that state-of-the-art NLU ploit contextual information. For instance, the en- models often make use of surface patterns in the tity Gonzales in the first sentence of the figure is data that do not generalize well. Named-Entity wrongly recognized as a person, while the context Recognition (NER), a downstream task that clearly signals that it is a location (city). consists in identifying textual mentions and To better highlight this issue, we propose NRB, classifying them into a predefined set of types, is a testbed designed to accurately diagnose name no exception. regularity bias of NER models by harvesting nat- The robustness of modern NER models has ural sentences from Wikipedia that contain chal- received considerable attention recently (May- lenging entities, such as those in Figure 1. This is hew et al., 2019, 2020; Agarwal et al., 2020a; different from previous works that evaluate mod- Zeng et al., 2020; Bernier-Colborne and Langlais, els on artificial data obtained by either random- 2020). Name Regularity Bias (Lin et al., 2020; izing (Lin et al., 2020) or substituting entities by ones from a pre-defined list (Agarwal et al., related works along two axes: diagnosis and miti- 2020a). NRB is compatible with any annotation gation. scheme, and is intended to be used as an auxiliary validation set. 2.1 Diagnosing Biais We conduct experiments with the feature-based A growing number of studies (Zellers et al., 2018; LSTM-CRF architecture (Peters et al., 2018; Ak- Poliak et al., 2018; Geva et al., 2019; Utama bik et al., 2018) and the BERT (Devlin et al., 2019) et al., 2020b; Sanh et al., 2020) are showing fine-tuning approach trained on standard bench- that NLU models rely heavily on spurious cor- marks. The best LSTM-based model we tested relations between output labels and surface fea- is able to correctly predict 38% of the entities in tures (e.g. keywords, lexical overlap), impacting NRB. BERT-based models are performing much their generalization performance. Therefore, con- better (+37%), even if they (slightly) underper- siderable attention has been paid to design diag- form on in-domain development and test sets. This nostic benchmarks where models relying on bias mismatch in performance between NRB and stan- would perform poorly. For instance, HANS (Mc- dard benchmarks indicates that context awareness Coy et al., 2019), FEVER Symmetric (Schus- of models is not rewarded by existing benchmarks, ter et al., 2019), and PAWS (Zhang et al., 2019) thus justifying NRB as an additional validation set. are benchmarks that contain counterexamples to We further propose a novel architecture- well-known biases in the training data of textual agnostic adversarial training procedure (Miyato entailment (Williams et al., 2017), fact verifica- et al., 2016) in which learnable noise vectors are tion (Thorne et al., 2018), and paraphrase identi- added to named-entity words, weakening their sig- fication (Wang et al., 2018) respectively. nal, thus encouraging the model to pay more atten- tion to contextual information. Applying it to both Naturally, many entity names have a strong cor- feature-based LSTM-CRF and fine-tuned BERT relation with a single type (e.g.
Taggers 3.1 Implementation weak supervision ORG (confidence: 0.97) We used the Stanford CoreNLP (Manning et al., context-only PER: 0.58, ORG: 0.30, LOC: 0.12 2014) tagger as our weak supervision tagger and Figure 2: Selection of a sentence in NRB. developed a simple yet efficient method to build a context-only tagger. For this, we first applied the The strategy used to gather examples in NRB is Stanford tagger to the entire Wikipedia dump and illustrated in Figure 2. We first select Wikipedia replaced all entity mentions identified by their tag. articles that are listed in a disambiguation page. Then, we train a 5-gram language model on the re- Disambiguation pages group different topics that sulting corpus using kenLM (Heafield, 2011). Fig- could be referred to by the same query term.1 The ure 3 illustrates how this model is deployed as an 1https://en.wikipedia.org/ entity tagger: the mention is replaced by an empty wiki/Wikipedia:Manual_of_Style/ slot and the language model is queried for each Disambiguation_pages. type. We rank the tags using the perplexity score given by the model to the resulting sentences, then a domain shift between NRB (Wikipedia) and we normalize those scores to get a probability dis- whatever dataset a model is trained on (we call it tribution over types. the in-domain corpus). WTS is composed of 5192 sentences that have also been manually checked. Obama is located in far southwestern Fukui Prefecture. 4 Diagnosing Bias is located in far southwestern Fukui Pre- 4.1 Data fecture. To be comparable with state-of-the-art models, {LOC: 0.61, ORG: 0.28, PER: 0.11} we consider two standard benchmarks for NER: CONLL-2003 (Tjong Kim Sang and De Meulder, Figure 3: Illustration of a language model used as 2003) and ONTONOTES 5.0 (Pradhan et al., 2012) a context-only tagger. which include 4 and 18 types of named-entities respectively. ONTONOTES is 4 times larger than We downloaded the Wikipedia dump of June CONLL, and both benchmarks mainly cover the 2020, which contains 30k disambiguation pages. news domain. We run experiments on the offi- These pages contain links to 263k articles, where cial train/dev/test splits, and report mention-level only 107k (40%) of them have a type in Freebase F1 scores, following previous works. Since in that can be mapped to the 3 types of interest. The NRB, there is only one entity per sentence to an- Stanford tagger identified 440k entities that match notate, a system is evaluated on its ability to cor- the query term of the disambiguation pages. The rectly identify the boundaries of this entity and its thresholds discussed previously were chosen to se- type. When we train on ONTONOTES (18 types) lect around 5000 of the most challenging exam- and evaluate on NRB (3 types), we perform type ples in terms of name regularity bias. This fig- mapping using the scheme of Augenstein et al. ure aligns with the number of entities present in (2017). the test set of the well-studied CONLL bench- mark (Tjong Kim Sang and De Meulder, 2003). 4.2 Systems We assessed the annotation quality, by asking Following (Devlin et al., 2019), we term all ap- a human to filter out noisy examples. A sentence proaches that learn the encoder from scratch as was removed if it contains an annotation error, or feature-based, as opposed to the ones that fine- if the type of the query term cannot be inferred tune a pre-trained model for the downstream task. from the local context. Only 1.3% of the exam- We conduct experiments using 3 feature-based ples where removed, which confirms the accuracy and 2 fine-tuning approaches for NER: of our automatic procedure. NRB is composed of 5275 examples, and each sentence contains a sin- • Flair-LSTM An LSTM-CRF model that gle annotation (see Figure 1 for examples). uses FLAIR (Akbik et al., 2018) contextual- ized embeddings as main features. 3.2 Control Set (WTS) In addition to NRB, we collected a set of domain • ELMo-LSTM The LSTM-CRF tagging control sentences — called WTS for WITNESS — model of Peters et al. (2018) that uses ELMo that contain the very same query terms selected in contextualized embeddings at the input layer. NRB, but which were correctly labeled by both the • BERT-LSTM Similar to the previous model, Stanford (score > 0.85) and the context-only tag- but replacing ELMo by a representation gath- gers. We selected examples with a small gap (< ered from the last four layers of BERT. 0.1) between the first and second ranked type as- signed to the query term by the latter tagger. Thus, • BERT-base The fine-tuning approach pro- examples in WTS should be easy to tag. For ex- posed by Devlin et al. (2019) using the ample, because Obama the Japanese city (see Fig- BERT-base model. ure 3) is selected among the query terms in NRB, we added an instance of Obama the president. • BERT-large The fine-tuning approach using Performing poorly on such examples2 indicates the BERT-large model. 2That is, a system that fail to tag Obama the president as a person. CONLL ONTONOTES Model Dev Test NRB WTS Dev Test NRB WTS Feature-based Flair-LSTM - 93.03 27.56 99.58 - 89.06 33.67 93.98 ELMo-LSTM 96.69 92.47 31.65 98.24 88.31 89.38 34.34 94.90 BERT-LSTM 95.94 91.94 38.34 98.08 86.12 87.28 43.07 92.04 Fine-tuning BERT-base 96.18 92.19 75.54 98.67 87.23 88.19 75.34 94.22 BERT-large 96.90 92.86 75.55 98.51 89.26 89.93 75.41 95.06
Table 1: Mention level F1 scores of models on CONLL and ONTONOTES, as well as on NRB and WTS.
We used Flair-LSTM off-the-shelf,3 and re- forming on in-domain test sets. This may be be- implemented other approaches using the default cause BERT was pre-trained on Wikipedia (same settings proposed in the respective papers. For our domain of NRB), while ELMo embeddings were reimplementations, we used early stopping based trained on the One Billion Word corpus (Chelba on performance on the development set, and re- et al., 2014). Also, we observe that switching port average performance over 5 runs. For BERT- from BERT-base to BERT-large, or training on 4 based solutions, we adopt spanBERT (Joshi et al., times more data (CONLL versus ONTONOTES) 2020) as a backbone model since it was found by does not help on NRB. This suggests that name Li et al. (2020) to perform better on NER. regularity bias is neither a data nor a model capac- ity issue. 4.3 Results 4.4 Feature-based vs. Fine-tuning Table 1 shows the mention level F1 score of the systems considered. FLAIR-LSTM and BERT- In this section, we analyze reasons for the drastic large are the best performing models on in-domain superiority of fined-tuned models on NRB. First, test sets, the maximum gap with other models be- the large gap between BERT-LSTM and BERT- ing 1.1 and 2.7 on CONLL and ONTONOTES re- base on NRB suggests that this is not related to spectively. These figures are in line with pre- the representations being used at the input layer. vious works. What is more interesting is the Second, we tested several configurations of performance on NRB. Feature-based models do ELMo-LSTM where we scale up the number of poorly, Flair-LSTM underperforms compared to LSTM layers and hidden units. We observed a other models (F1 score of 27.6 and 33.7 when degradation of performance on dev, test and NRB trained on CONLL and ONTONOTES respec- sets, mostly due to over-parameterized models. tively). Fine-tuned BERT models clearly perform We also trained 9, 6 and 4 layers BERT-base mod- 4 better (around 75), but far from in-domain re- els, and still noticed a large advantage of BERT 5 sults (92.9 and 89.9 on CONLL and ONTONOTES models on NRB. This suggests that the higher ca- respectively). Domain shift is not a reason for pacity of BERT alone can not explain all the gains. those results, since the performances on WTS are Third, since by design, evidences on the entity rather high (92 or higher). Furthermore, we found type in NRB reside within the local context, it is that the boundary detection (segmentation) perfor- unlikely that gains on this set come from the abil- mance on NRB is above 99.2% across all settings. ity of Transformers (Vaswani et al., 2017) to better Since errors made on NRB are neither due to seg- handle long dependencies than LSTM (Hochreiter mentation nor to domain shift, they must be im- and Schmidhuber, 1997). To further validate this puted to name regularity bias of models. statement, we fine-tuned BERT models with ran- It is worth noting that BERT-LSTM outper- domly initialized weights, except the embedding forms ELMo-LSTM on NRB, despite underper- 4We used early exit (Xin et al., 2020) at the kth layer. 5The 4-layer model has 53M parameters and performs 3https://github.com/flairNLP/flair 52% on NRB. layer. We noticed that this time, the performances the model to infer the type of masked tokens from on NRB fall into the same range of those of the context. We call this procedure mask. feature-based models, and a drastic decrease (12- 15%) on standard benchmarks. These observa- 5.2 Parameter Freezing tions are in keeping with results from (Hendrycks Another simple strategy, specific to fine-tuning et al., 2020) on the out-of-distribution robustness BERT, consists of freezing part of the network. of fine-tuning pre-trained transformers, and also More precisely, we freeze the bottom half of confirms observations made by (Agarwal et al., BERT, including the embedding layer. The in- 2020b). tuition is to preserve part of the predicting-by- From these analyses, we conclude that the context mechanism that BERT has acquired during Masked Language Model (MLM) objective (De- the pre-training phase. This training procedure is vlin et al., 2019) that the BERT models were pre- expected to enforce the contextual ability of the trained with is a key factor driving superior per- model, thus adding to our analysis on the critical formances of the fine-tuned models on NRB. In role of the MLM objective in pre-training BERT. most cases, the target word is masked or randomly We name this method freeze. selected, therefore the model must rely on the con- text to predict the correct target, which is what 5.3 Adversarial Noise a model should do to correctly predict the type We propose an adversarial learning algorithm that of entities in NRB. We think that in fine-tuning, makes entity type patterns in the input representa- training for a few epochs with a small learning tion less reliable for the model, thus enforcing it rate, helps the model to preserve the contextual be- to rely more aggressively on the context. To do so, haviour induced by the MLM objective. we add a learnable adversarial noise vector (only) Nevertheless, fine-tuned models recording at to the input representation of entities. We refer to best an F1 score of 75.6 on NRB do show some this method as adv. name regularity bias, and fail to capture useful lo- Let T = {t1, t2, . . . , tK } be a predefined set of cal contextual information. types such as PER, LOC, and ORG in our case. Let x = x1, x2, . . . , xn be the input sequence of 5 Mitigating Bias length n, y = y1, y2, . . . , yn be the gold label se- quence following the IOB6 tagging scheme, and In this section, we investigate training procedures 0 0 0 0 y = y1, y2, . . . , yn be a sequence obtained by that are designed to enhance the contextual aware- adding noise to y at the mention-level, that is, by ness of a model, leading to a better performance on randomly replacing the type of mentions in y with NRB without impacting in-domain performance. some noisy type sampled from T . These training procedures are not supposed to use Let Yij(t) = yi, . . . , yj be a mention of type any external data. In fact, NRB is only used as t ∈ T , spanning the sequence of indices i to j a diagnosing corpus, once the model is trained. 0 0 in y. We derive a noisy mention Y ij in y from We propose 3 training procedures that can be com- Yij(t) as follows: bined, two of them are architecture-agnostic, and one is specific to fine-tuning BERT. 0 Yij(t ) p ∼ U(0, 1) ≤ λ 5.1 Entity Masking 0 t0 ∼ Cat (γ|ξ = 1 ) Yij = K−1 Inspired by the masking strategy applied during γ∈T \{t} the pre-training phase of BERT, we propose a data Yij(t) otherwise augmentation approach that introduces a special [MASK] token in some of the training examples. where λ is a threshold parameter, U(0, 1) refers Specifically, we search for entities in the training to the uniform distribution in the range [0,1], 1 material that are preceded or followed by 3 non- Cat(γ|ξ = K−1 ) is the categorical distribution entity words. This criterion applies to 35% and whose outcomes are equally likely with the proba- 0 0 0 39% of entities in the training data of CONLL and bility of ξ, and the set T \{t} = {t : t ∈ T ∧t 6= ONTONOTES respectively. For each such entity, t} stands for the set T excluding type t. we create a new training example (new sentence) 6Naturally applies to other schemes, such as BILOU that by replacing the entity by [MASK], thus forcing Ratinov and Roth (2009) found more informative. True Noisy True Noisy Tag Tag Tag Tag B-PER B-PER O O O O O O B-LOC B-PER I-LOC I-PER
Output layer
Location pattern recovered via context Sequence Encoder Ambiguous Person pattern
LOC -> PER Noise Embeddings 0 0 0 0 noise embedding + + + + + + Input Embeddings Location pattern John was born in New York
Figure 4: Illustration of our adversarial method applied on the entity New York. First, we generate a noisy type (PER), and then add a learnable noise embedding (LOC→PER) to the input representation of that entity. This will make entity patterns (hashed rectangles) unreliable for the model, hence forcing it to collect evidences (dotted arrow) from the context. The noise embedding matrix and the noise label projection layer weights (dotted rectangle) are trained independently from the model parameters.
The above procedure only applies to the enti- is the loss on the noisy tags defined as follows: ties which are preceded or followed by 3 context words. For instance, in Figure 4, we produce a n 0 X 1 0 0 0 noisy type for New York (PER), but not for John Lnoisy(θ ) = (yi 6= yi) CE(fi , yi) (p > λ). Also, note that we generate a different i=1 sequence y0 from y at each training epoch. where CE is the cross-entropy loss function. Next, we define a learnable noisy embedding Both losses are minimized using gradient descent. 0 m×d matrix E ∈ R where m = |T | × (|T | − 1) It is worth mentioning that λ is the only hyper- is the number of valid type switching possibilities, parameter of our adv method. It controls how and d is the dimension of the input representations often noisy embeddings are added during train- of x. For each token with a noisy label, we add the ing. Higher values of λ increase the amount of corresponding noisy embedding to its input repre- uncertainty around salient patterns in the input sentation. For other tokens, we simply add a zero representation of entities, hence preventing the vector of size d. As depicted in Figure 4, the noisy model from overfitting those patterns, and there- type of the entity New York is PER, therefore we fore pushing it to rely more on context informa- add the noise embedding at index LOC → PER tion. We tried values of λ between 0.3 and 0.9, to its input representation. and found λ = 0.8 to be the best one based on Then, the input representation of the sequence is CONLL and ONTONOTES development sets. fed to an encoder followed by an output layer, such 5.4 Results as LSTM-CRF in (Peters et al., 2018), or BERT- Softmax in (Devlin et al., 2019). First, we extend We trained models on CONLL and ONTONOTES, 7 the aforementioned models by generating an extra and evaluated them on their respective TEST set. logit f 0 using a projection layer parametrized by Recall that NRB and WTS are only used as auxil- W 0 and followed by a softmax function. As shown iary diagnosing sets. Table 2 shows the impact of in Figure 4, for each token the model produces two our training methods when fine-tuning the BERT- logits relative to the true and noisy tags. Then, large model (the one that performs best on NRB). we train the entire model to minimize two losses: First, we observe that each training method 0 significantly improves the performance on NRB. Ltrue(θ) and Lnoisy(θ ), where θ is the original set of parameters and θ0 = {E0,W 0} is the extra set Adding adversarial noise is notably the best per- forming method on NRB, with an additional gain we added (dotted boxes in Figure 4). Ltrue(θ) is 0 7 the regular loss on the true tags, while Lnoisy(θ ) Performances on DEV show very similar trends. of 10.5 and 10.4 F1 points over the respective those observed with BERT-large: significant gains baselines. On the other hand, we observe minor on NRB of 14 and 12 points for CONLL and variations on in-domain test sets, as well as on ONTONOTES models respectively, and no statis- WTS. The paired sample t-test (Cohen, 1996) con- tically significant changes on in-domain test sets. firms that these variations are not statistically sig- Again, combining training methods leads to sys- nificant (p > 0.05). After all, the number of deci- tematic gains on NRB (13 points on average). sions that differ between the baseline and the best Differently from fine-tuning BERT, we observe a model on a given in-domain set is less than 20. slight drop in performance of 1.2% on WTS when both methods are used. CONLL ONTONOTES The performance of ELMo-LSTM on NRB Method Test NRBWTS Test NRBWTS does not rival with the one obtained by fine- tuning the BERT-large model, which confirms that BERT-lrg 92.8 75.6 98.6 89.9 75.4 95.1 BERT is a key factor to enhance robustness, even +mask 92.9 82.9 98.4 89.8 77.3 96.5 if in-domain performance is not necessarily re- +freeze 92.7 83.1 98.4 89.9 79.8 96.0 warded (McCoy et al., 2019; Hendrycks et al., +adv 92.7 86.1 98.3 90.1 85.8 95.2 2020). +f&m 92.8 85.5 97.8 89.9 80.6 95.9 +a&m 92.8 87.7 98.1 89.7 87.6 95.9 6 Analysis +a&f 92.7 88.4 98.2 90.0 88.1 95.7 +a&m&f 92.8 89.7 97.9 89.9 88.8 95.6 So far, we have shown that state-of-the-art mod- els do suffer from name regularity bias, and we Table 2: Impact of training methods on proposed model-agnostic training methods which BERT-large models fine-tuned on CONLL or are able to mitigate this bias to some extent. In ONTONOTES. Section 6.1, we provide further evidences that our training methods force the BERT-large model to Second, we observe that combining methods better concentrate on contextual cues. In Sec- always leads to improvements on NRB; the best tion 6.2, we replicate the evaluation protocol of configuration being when we combine all 3 meth- Lin et al. (2020) in order to clear out the possibility ods. It is interesting to note that combining that our training methods are only valid on NRB. training methods leads to a performance on NRB Last, we perform extensive experiments on name which does not depend much on the training set regularity bias under low resource (Section 6.3) used: CONLL (89.7) and ONTONOTES (88.8). and multilingual (Section 6.4) settings. This suggests that name regularity bias is a mod- elling issue, and not the effect of factors such as 6.1 Attention Heads training data size, domain, or type granularity. We leverage the attention map of BERT to bet- ter understand how our method enhances context CONLL ONTONOTES Method encoding. To this end, we calculate the average Test NRBWTS Test NRBWTS number of attention heads that point to the entity E-LSTM 92.5 31.7 98.2 89.4 34.3 94.9 mentions being predicted at each layer. We con- +mask 92.4 40.8 97.5 89.3 38.8 95.3 duct this experiment on NRB with the BERT-large +adv 92.4 42.4 97.8 89.4 40.7 95.0 model (24 layers with 16 attention heads at each +a&m 92.4 45.7 96.8 89.3 46.6 93.7 layer) fine-tuned on CONLL. At each layer, we average the number of Table 3: Impact of training methods on the ELMo- heads which have their highest attention weight 8 LSTM trained on CONLL or ONTONOTES. (argmax) pointing to the entity name. Figure 5 shows the average number of attention heads that In order to validate that our training methods point to an entity mention in the BERT-large are not specific to the fine-tuning approach, we model fine-tuned without our methods, with the replicated the same experiments with the ELMo- adversarial noise method (adv), and with all three LSTM. Table 3 shows the performances of the methods. mask and adv procedures (the freeze method does 8We used the weights of the first sub-token since NRB not apply here). The results are in line with only contains single word entities. 15 Table 4 shows the results of the BERT-large BERT-large +adv model fine-tuned on CONLL and evaluated on 10 +all methods the permuted in-domain dev and test sets. F1 5 scores are much lower here, confirming this is # Attn Heads a hard testbed, but they do provide evidences of 0 0 5 10 15 20 the named-regularity bias of BERT. Our training Layer Id methods improve the model F1 score by 17% and 13% on permuted dev and test sets respectively, Figure 5: Average number of attention heads (y- an increase much inline with what we observed on axis) pointing to NRB entity mentions at each NRB. layer (x-axis) of the BERT-large model fine-tuned on CONLL. 6.3 Low Resource Setting
We observe an increasing number of heads Similarly to (Zhou et al., 2019; Ding et al., 2020), pointing to entity names when we get closer to we simulate a low resource setting by randomly the output layer: at the bottom layers (left part of sampling tiny subsets of the training data. Since the figure) only a few heads are pointing to en- our focus is to measure the contextual learning tity names, in contrast to the last 2 layers (right ability of models, we first selected sentences of part) where almost all heads do so. This observa- CONLL training data that contain at least one en- tion is inline with Jawahar et al. (2019) who show tity followed or preceded by 3 non-entity words. that bottom and intermediate BERT layers mainly encode lexical and syntactic information, while 80 BERT-large top layers represent task-related information. Our +adv training methods lead to less heads at top layers 60 +all methods 40 pointing to entity mentions, suggesting the model F1 score is focusing more on contextual information. 20
100 500 1000 2000 all number of sentences 6.2 Random Permutations
Following the protocol described in (Lin et al., Figure 6: Performance on NRB of BERT-large 2020), we modified dev and test sets of standard models as a function of the number of sentences benchmarks by randomly permuting dataset-wise used to fine-tune them. mentions of entities, keeping the types untouched. For instance, the span of a specific mention of a Then, we randomly sampled k ∈ person can be replaced by a span of a location, 9 whenever it appears in the dataset. These random- {100, 500, 1000, 2000} sentences with which ized tests are highly challenging, as discussed in we fine-tuned BERT-large. Figure 6 shows the Section 2, since here the context is the only avail- performance of the resulting models on NRB. able clue to solve the task, and many false positive Expectedly, F1 scores of models fine-tuned with examples are introduced that way. few examples are rather low on NRB as well as on the in-domain test set. Not shown in Figure 6, fine-tuning on 100 and 2000 sentences leads to Method π(dev) π(test) performance of 14% and 45% respectively on BERT-large 23.45 25.46 the CONLL test set. Nevertheless, we observe +adv 31.98 31.99 that our training methods, and adv in particular, +adv&mask 35.02 34.09 improve performances on NRB even under ex- +adv&mask&freeze 40.39 38.62 tremely low resource settings. On CONLL test and WTS sets, scores vary in a range of ±0.5 and Table 4: F1 scores of BERT-large models fine- ±0.7 respectively when our methods are added to tuned on CONLL and evaluated on randomly per- BERT-large. muted versions of the dev and test sets: π(dev) and π(test). 9{0.7, 3.5, 7.1, 14.3}% of the training sentences. 6.4 Multilingual Setting language, as well as the existence of type informa- tion. This is left as future work. 6.4.1 Experimental Protocol For experiments with fine-tuning, we use For in-domain data, we use the German, Spanish, language-specific BERT models11 for Ger- and Dutch CONLL-2002 (Tjong Kim Sang, 2002) man (Chan et al., 2020), Spanish (Canete et al., NER datasets. Those benchmarks — also from the 2020), Dutch (de Vries et al., 2019), Finnish (Vir- news domain — come with a train/dev/test split, tanen et al., 2019), Danish12, Croatain (Ulcarˇ and the training material is comparable in size and Robnik-Šikonja, 2020), while we use to the English CONLL dataset. In addition, we mBERT (Devlin et al., 2019) for Afrikaans. experiment with four non CONLL benchmarks: For feature-based approaches, we use the same Finnish (Luoma et al., 2020), Danish (Hvingelby architecture for ELMo-LSTM (Peters et al., 2018) et al., 2020), Croatian (Ljubešic´ et al., 2018), and except that we replace English word embeddings Afrikaans (Eiselen, 2016) data. These corpora by language-specific ones: FastText (Bojanowski have more diversified text genres, yet mainly fol- et al., 2017) for static representations, and the 10 low the CONLL annotation scheme. Finnish aforementioned BERT-base models for contextu- and Afrikaans datasets have comparable size to alized ones. English CONLL, Danish is 60% smaller, while the Croatian is twice larger. We use the pro- 6.4.2 Results vided train/dev/test splits for Danish and Finnish, Table 6 reports the performances on test, NRB, while we randomly split (80/10/10) the Croatian and WTS sets for both feature-based and fine- and Afrikaans datasets. tuning approaches with and without our training Since NRB and WTS are in English, we de- methods. We used the hyper-parameters of the En- signed a simple yet generic method for projecting glish CONLL experiments with no further tuning. them to another language. First, both test sets are We selected the best performing models based on translated to the target language using an online development sets score, and report average results translation service. In order to ensure a high qual- on 5 runs. ity corpus, we eliminate a sentence if the BLEU Mainly due to implementation details and score (Papineni et al., 2002) between the original hyper-parameter settings, our fine-tuned BERT- (English) sentence and the back translated one is base models perform better on the CONLL test below 0.65. sets for German (83.8 vs. 80.4) and Dutch (91.8 vs. 90.0) and slightly worse on Spanish (88.0 vs. NRB WTS NRB WTS 88.4) compared to the results reported in their re- de 37% 44% fi 53% 62% spective BERT papers. es 20% 22% da 19% 24% Consistent with the results obtained on English nl 20% 24% hr 39% 48% for feature-based (Table 1) and fine-tuned (Ta- af 26% 32% ble 3) models, the latter approach performs better on NRB, although by a smaller margin compared Table 5: Percentage of translated sentences from to English (+37%). More precisely, we observe a NRB and WTS discarded for each language. gain of +28% and +26% on German and Croatian respectively, and a gain ranging between 11% and Table 5 reports the percentage of discarded sen- 15% for other languages. tences for each language. While for the Finnish Nevertheless, our training methods lead to sys- (fi), Croatian (hr) and German (de) languages we tematic and often drastic improvements on NRB remove a large proportion of sentences, we found coupled with a statistically non significant over- our translation approach more simple and system- all decrease on in-domain test sets. They do how- atic than generating an NRB corpus from scratch ever incur a slight but significant drop of around for each language. The latter approach depends on 2 F1 score points on WTS for feature-based mod- the robustness of the weak tagger, the number of 11Language-specific models have been reported more ac- Wikipedia articles and disambiguation pages per curate than multilingual ones in a monolingual setting (Mar- tin et al., 2019; Le et al., 2020; Delobelle et al., 2020; Virta- 10The Finnish data is tagged with EVENT, PRODUCT and nen et al., 2019). DATE in addition to the CONLL 4 classes. 12https://github.com/botxo/nordic_bert German Spanish Dutch Finnish Danish Croatian Afrikaans Model TESTNRBWTS TESTNRBWTS TESTNRBWTS TESTNRBWTS TESTNRBWTS TESTNRBWTS TESTNRBWTS Feature-based BERT-LSTM 78.9 36.4 84.2 85.6 59.9 90.8 84.9 45.4 85.7 76.0 38.9 84.5 76.4 42.6 78.1 78.0 28.4 79.3 76.2 39.7 65.8 +adv 78.2 44.1 82.8 85.0 65.8 90.2 84.3 57.8 83.5 75.1 52.9 81.0 75.4 47.2 76.9 77.5 35.2 75.5 75.7 42.3 63.3 +adv&mask 78.1 47.6 82.9 84.9 72.2 88.7 84.0 62.8 83.5 74.6 54.3 81.8 75.1 48.4 76.6 76.9 36.8 76.7 75.1 52.8 63.1 Fine-tuning BERT-base 83.8 64.0 93.3 88.0 72.3 93.9 91.8 56.1 92.0 91.3 64.6 91.9 83.6 56.6 86.2 89.7 54.7 95.6 80.4 54.3 91.6 +adv 83.7 68.9 93.6 87.9 75.9 93.9 91.9 58.3 91.8 90.2 66.4 92.5 82.7 58.4 86.5 89.5 57.9 95.5 79.7 60.2 92.1 +a&m&f 83.2 73.3 94.0 87.4 81.6 93.7 91.2 63.6 91.0 89.8 67.4 92.7 82.3 63.1 85.4 88.8 59.6 94.9 79.4 64.2 91.6
Table 6: Mention level F1 scores of 7 multilingual models trained on their respective training data, and tested on their respective in-domain test, NRB, and WTS sets. els. Similar to what was previously observed, the This study opens up new avenues of investiga- best scores on NRB are obtained by BERT models tions. Conducting a large-scaled multilingual ex- when the training methods are combined. For the periment, characterizing the name regularity bias Dutch language, we observe that once trained with of more diversified morphological language fam- our methods, the type of models used (feature- ilies is one of them, possibly leveraging mas- based versus BERT fine-tuned) leads to much less sively multilingual resources such as WikiAnn difference on NRB. (Pan et al., 2017), Polyglot-NER (Al-Rfou et al., Altogether, these results demonstrate that name 2015), or Universal Dependencies (Nivre et al., regularity bias is not specific to a particular lan- 2016). We can also develop a more challenging guage, even if its degree of severity varies from NRB by selecting sentences with multi-word enti- one language to another, and that the training ties. methods proposed notably mitigate this bias. Also, non-sequential labelling approaches for NER like the ones of (Li et al., 2020; Yu et al., 7 Conclusion 2020) have reported impressive results on both flat In this work, we focused on the name regularity and nested NER. We plan to measure their bias bias of NER models, a problem first discussed in on NRB and study the benefits of applying our (Lin et al., 2020). We propose NRB, a benchmark training methods to those approaches. Finally, we we specifically designed to diagnose such a bias. want to investigate whether our adversarial train- As opposed to existing strategies devised to mea- ing method can be successfully applied to other sure it, NRB is composed of real sentences with NLP tasks. easy to identify mentions. 8 Acknowledgments We show that current state-of-the-art models, perform from poorly (feature-based) to decently We are grateful to the reviewers of this work (fined-tuned BERT) on NRB. In order to mitigate for their constructive comments that greatly con- this bias, we propose a novel adversarial training tributed to improving this paper. method based on adding some learnable noise vec- tors to entity words. These learnable vectors en- courage the model to better incorporate contex- References tual information. We demonstrate that this ap- Oshin Agarwal, Yinfei Yang, Byron C Wallace, proach greatly improves the contextual ability of and Ani Nenkova. 2020a. Entity-Switched existing models, and that it can be combined with Datasets: An Approach to Auditing the In- other training methods we proposed. Significant Domain Robustness of Named Entity Recogni- gains are observed in both low-resource and mul- tion Models. arXiv preprint arXiv:2004.04123. tilingual settings. To foster research on NER ro- bustness, we encourage others to report results on Oshin Agarwal, Yinfei Yang, Byron C Wallace, NRB and WTS.13 able at http://rali.iro.umontreal.ca/rali/ 13English and multilingual NRB and WTS are avail- ?q=en/wikipedia-nrb-ner and Ani Nenkova. 2020b. Interpretability anal- Proceedings of The 12th Language Resources ysis for named entity recognition to understand and Evaluation Conference, pages 1697–1704, system predictions and how they can improve. Marseille, France. European Language Re- arXiv preprint arXiv:2004.04564. sources Association.
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