DOCSLIB.ORG

Unsupervised Sentence Compression Using Denoising Auto-Encoders

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Unsupervised Sentence Compression Using Denoising Auto-Encoders Unsupervised Sentence Compression using Denoising Auto-Encoders Thibault Fevry∗ Jason Phang∗ Center for Data Science Center for Data Science New York University New York University [email protected] [email protected] Sentence compression can also be can be seen Abstract as a “scaled down version of the text summariza- tion problem” (Knight and Marcu, 2002). Within In sentence compression, the task of short- text summarization, two broad approaches exist: ening sentences while retaining the original extractive approaches extract explicit tokens or meaning, models tend to be trained on large phrases from the reference text, whereas abstrac- corpora containing pairs of verbose and compressed sentences. To remove the need for tive approaches involve a compressed paraphras- paired corpora, we emulate a summarization ing of the reference text, similar to the approach task and add noise to extend sentences and humans might take (Jing, 2000, 2002). train a denoising auto-encoder to recover the In the related domain of machine translation, a original, constructing an end-to-end training task that also involves learning a mapping from regime without the need for any examples one string of tokens to another, state of the art of compressed sentences. We conduct a models using deep learning techniques are trained human evaluation of our model on a standard on large parallel corpora. Recent promising text summarization dataset and show that it performs comparably to a supervised base- work on unsupervised neural machine translation line based on grammatical correctness and (Artetxe et al., 2017; Lample et al., 2017) has retention of meaning. Despite being exposed shown that with the right training regime, it is to no target data, our unsupervised models possible to train models for machine translation learn to generate imperfect but reasonably between two languages given only two unpaired readable sentence summaries. Although we monolingual corpora. underperform supervised models based on In this paper, we apply neural text summariza- ROUGE scores, our models are competitive with a supervised baseline based on human tion techniques to the task of sentence compres- evaluation for grammatical correctness and sion, focusing on on extractive summarization. retention of meaning. However, we depart significantly from prior work by taking a fully unsupervised training approach. Beyond not using parallel corpora, we train our 1 Introduction model using a single corpus. In contrast to un- supervised neural machine translation, which still Sentence compression is the task of condensing uses two corpora, we do not have separate corpora a longer sentence into a shorter one that still re- of longer and shorter sentences. tains the meaning of the original. Past models for We show that a simple denoising auto-encoder sentence compression have tended to rely heavily model, trained on removing and reordering words on strong linguistic priors such as syntactic rules from a noised input sequence, can learn effec- or heuristics (Dorr et al., 2003; Cohn and Lap- tive sentence compression, generating shorter se- ata, 2008). More recent work using deep learning quences of reasonably grammatical text that retain involves models trained without strong linguistic the original meaning. While the models are still priors, instead requiring large corpora consisting prone to both errors in grammar and meaning, we of pairs of longer and shorter sentences (Miao and believe that this is a strong step toward reducing Blunsom, 2016). reliance on paired corpora. ∗ Denotes equal contribution We evaluate our model using both a stan- 413 Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 413–422 Brussels, Belgium, October 31 - November 1, 2018. c 2018 Association for Computational Linguistics dard text-summarization benchmark as well as hu- on at least 500k instances of paired data. man evaluation of compressed sentences based on In machine translation, unsupervised methods grammatical correctness and retention of mean- for aligning word embeddings using only un- ing. Although our models do not capture the matched bilingual corpora, trained with only small written style of the target summaries (headlines), seed dictionaries, (Mikolov et al., 2013; Lazari- they still produce reasonably readable and accu- dou et al., 2015), adversarial training on simi- rate compressed sentence summaries, without ever lar corpora (Zhang et al., 2017; Conneau et al., being exposed to any target sentence summaries. 2017b) or even on distant corpora and languages We find that our model underperforms based on (Artetxe et al., 2018) have enabled the develop- ROUGE metrics, especially compared to super- ment of unsupervised machine translation (Artetxe vised models, but performs competitively with su- et al., 2017; Lample et al., 2017). However, it pervised baselines in human evaluation. We fur- is not clear how to adapt these methods for sum- ther show that providing the model with a sen- marization where the task is to shorten the refer- tence embedding of the original sentence leads ence rather than translate it. Wang and Lee(2018) to better ROUGE scores but worse human eval- train a generative adversarial network to encode uation scores. However, both unsupervised and references into a latent space and decode them supervised methods still fall short based on hu- in summaries using only unmatched document- man evaluation, and effective sentence compres- summary pairs. However, in contrast with ma- sion and summarization remains an open problem. chine translation where monolingual data is plen- tiful and paired data scarce, summaries are paired 2 Related work with their respective documents when they exist, thus limiting the usefulness of such approaches. Early sentence compression approaches were In contrast, our method requires no summary cor- extractive, focusing on deletion of uninforma- pora. tive words from sentences through learned rules (Knight and Marcu, 2002) or linguistically- Denoising auto-encoders (Vincent et al., 2008) motivated heuristics (Dorr et al., 2003). The first have been successfully used in natural language abstractive approaches also relied on learned syn- processing for building sentence embeddings (Hill tactic transformations (Cohn and Lapata, 2008). et al., 2016), training unsupervised translation Recent work in automated text summarization models (Artetxe et al., 2017) or for natural lan- has seen the application of sequence-to-sequence guage generation in narrow domains (Freitag and models to automatic summarization, including Roy, 2018). In all those instances, the added noise both extractive (Nallapati et al., 2017) and ab- takes the form of random deletion of words and stractive (Rush et al., 2015; Chopra et al., 2016; word swapping or shuffling. Although our noising Nallapati et al., 2016; Paulus et al., 2017; Fan mechanism relies on adding rather than removing et al., 2017) approaches, as well as hybrids of words, we take some inspiration from these works. both (See et al., 2017). Although these meth- Work in sentence simplification (see Shardlow ods have achieved state-of-the-art results, they are (2014) for a survey) has some similarities with constrained by their need for large amounts paired sentence compression, but it differs in that the document-summary data. key focus is on making sentences more easily un- Miao and Blunsom(2016) seek to overcome derstandable rather than shorter. Though word this shortcoming by training separate compressor deletion is used, sentence simplification methods and reconstruction models, allowing for training feature sentence splitting and word simplification based on both paired (supervised) and unlabeled which are not usually present in sentence compres- (unsupervised) data. For their compressor, they sion. Furthermore, these methods often rely heav- train a discrete variational auto-encoder for sen- ily on learned rules (e.g lexical simplification as tence compression and use the REINFORCE al- in Biran et al.(2011)), integer linear programming gorithm to allow end-to-end training. They fur- and sentence parse trees which makes them starkly ther use a pre-trained language model as a prior different from our deep learning-based approach. for their compression model to induce their com- The exceptions that adopt end-to-end approaches, pressed output to be grammatical. However, their such as Filippova et al.(2015), are usually super- reported results are still based on models trained vised and focus on word deletion. 414 3 Methods 3.3 Length Countdown To induce our model to output sequences of a de- 3.1 Model sired length, we augment the RNN decoder in our Our core model is based on a standard attentional model to take an additional length countdown in- encoder-decoder (Bahdanau et al., 2014), consist- put. In the context of text generation, RNN de- ing of multiple layers bi-directional long short- coders can be formulated as follows: term memory networks in both the encoder and decoder, with negative-log likelihood as our loss ht = RNN(ht−1; xt) (1) function. We detail below the training regime and model modifications to apply the denoising auto- where ht−1 is the hidden state at the previous step encoding paradigm to sentence compression. and xt is an external input (often an embedding of the previously decoded token). Let Tdec be the 3.2 Additive Noising desired length of our output sequence. We modify (1) with an additional input: Since we do not use paired sentence compression data with which to train our model in a supervised ht = RNN(ht−1; xt;Tdec − t) (2) way, we simulate a supervised training regime by modifying a denoising auto-encoder (DAE) train- The length countdown T − t is a single scalar in- ing regime to more closely resemble supervised put that ticks down to 0 when the decoder reaches sentence compression. Given a reference sen- the desired length T , and goes negative after. In tence, we extend and shuffle the input sentence, practice, (xt;Tdec − t) are concatenated into a sin- and then train our model to recover the original gle vector. We also experimented with adding a reference sentence.
Load more

Recommended publications
  • Deep Learning for Natural Language Processing Creating Neural Networks with Python
    Deep Learning for Natural Language Processing Creating Neural Networks with Python Palash Goyal Sumit Pandey Karan Jain Deep Learning for Natural Language Processing: Creating Neural Networks with Python Palash Goyal Sumit Pandey Bangalore, Karnataka, India Bangalore, Karnataka, India Karan Jain Bangalore, Karnataka, India ISBN-13 (pbk): 978-1-4842-3684-0 ISBN-13 (electronic): 978-1-4842-3685-7 https://doi.org/10.1007/978-1-4842-3685-7 Library of Congress Control Number: 2018947502 Copyright © 2018 by Palash Goyal, Sumit Pandey, Karan Jain Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the book’s product page, located at www.apress.com/978-1-4842-3684-0. For more detailed information, please visit www.apress.com/source-code. Contents Introduction�������������������������������������������������������������������������������������� xvii Chapter 1: Introduction to Natural Language Processing and Deep Learning ���������������������������������������������������������������������1 Python Packages ���������������������������������������������������������������������������������������������������3 NumPy �������������������������������������������������������������������������������������������������������������3 Pandas �������������������������������������������������������������������������������������������������������������8 SciPy ��������������������������������������������������������������������������������������������������������������13 Introduction to Natural
    [Show full text]
  • Text Feature Mining Using Pre-Trained Word Embeddings
    DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Text Feature Mining Using Pre-trained Word Embeddings HENRIK SJÖKVIST KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES Text Feature Mining Using Pre- trained Word Embeddings HENRIK SJÖKVIST Degree Projects in Financial Mathematics (30 ECTS credits) Degree Programme in Industrial Engineering and Management KTH Royal Institute of Technology year 2018 Supervisor at Handelsbanken: Richard Henricsson Supervisor at KTH: Henrik Hult Examiner at KTH: Henrik Hult TRITA-SCI-GRU 2018:167 MAT-E 2018:28 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract This thesis explores a machine learning task where the data contains not only numer- ical features but also free-text features. In order to employ a supervised classifier and make predictions, the free-text features must be converted into numerical features. In this thesis, an algorithm is developed to perform that conversion. The algorithm uses a pre-trained word embedding model which maps each word to a vector. The vectors for multiple word embeddings belonging to the same sentence are then combined to form a single sentence embedding. The sentence embeddings for the whole dataset are clustered to identify distinct groups of free-text strings. The cluster labels are output as the numerical features. The algorithm is applied on a specific case concerning operational risk control in banking. The data consists of modifications made to trades in financial instruments. Each such modification comes with a short text string which documents the modi- fication, a trader comment.
    [Show full text]
  • A Deep Reinforcement Learning Based Multi-Step Coarse to Fine Question Answering (MSCQA) System
    The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) A Deep Reinforcement Learning Based Multi-Step Coarse to Fine Question Answering (MSCQA) System Yu Wang, Hongxia Jin Samsung Research America fyu.wang1, [email protected] Abstract sentences, which helps reduce the computational workload. The fast model can be implemented using bag-of-words In this paper, we present a multi-step coarse to fine ques- (BoW) or convolutional neural network and the slow model tion answering (MSCQA) system which can efficiently pro- cesses documents with different lengths by choosing appro- uses an RNN based model. The model gives decent perfor- priate actions. The system is designed using an actor-critic mance on long documents in WIKIREADING dataset, and based deep reinforcement learning model to achieve multi- shows significant speed-up compared to earlier models. This step question answering. Compared to previous QA models coarse to fine model, however, still have several disadvan- targeting on datasets mainly containing either short or long tages: documents, our multi-step coarse to fine model takes the mer- 1.The model doesn’t perform as good as baseline models its from multiple system modules, which can handle both on the full WIKIREADING dataset including both short and short and long documents. The system hence obtains a much long documents (Hewlett et al. 2016). One reason is because better accuracy and faster trainings speed compared to the that the conventional RNN based models perform better on current state-of-the-art models. We test our model on four QA datasets, WIKEREADING, WIKIREADING LONG, CNN generating answers from the first few sentences of a doc- and SQuAD, and demonstrate 1.3%-1.7% accuracy improve- ument, hence can obtain the correct results on short docu- ments with 1.5x-3.4x training speed-ups in comparison to the ments more accurately than a coarse-to-fine model .
    [Show full text]
  • Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
    Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards Heriberto Cuayahuitl´ Donghyeon Lee Seonghan Ryu School of Computer Science Artificial Intelligence Research Group Artificial Intelligence Research Group University of Lincoln Samsung Electronics Samsung Electronics Lincoln, United Kingdom Seoul, South Korea Seoul, South Korea [email protected] [email protected] [email protected] Sungja Choi Inchul Hwang Jihie Kim Artificial Intelligence Research Group Artificial Intelligence Research Group Artificial Intelligence Research Group Samsung Electronics Samsung Electronics Samsung Electronics Seoul, South Korea Seoul, South Korea Seoul, South Korea [email protected] [email protected] [email protected] Abstract—Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text—without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of ≥10 sentences.
    [Show full text]
  • Abusive and Hate Speech Tweets Detection with Text Generation
    ABUSIVE AND HATE SPEECH TWEETS DETECTION WITH TEXT GENERATION A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science by ABHISHEK NALAMOTHU B.E., Saveetha School of Engineering, Saveetha University, India, 2016 2019 Wright State University Wright State University GRADUATE SCHOOL June 25, 2019 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPER- VISION BY Abhishek Nalamothu ENTITLED Abusive and Hate Speech Tweets Detection with Text Generation BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIRE- MENTS FOR THE DEGREE OF Master of Science. Amit Sheth, Ph.D. Thesis Director Mateen M. Rizki, Ph.D. Chair, Department of Computer Science and Engineering Committee on Final Examination Amit Sheth, Ph.D. Keke Chen, Ph.D. Valerie L. Shalin, Ph.D. Barry Milligan, Ph.D. Interim Dean of the Graduate School ABSTRACT Nalamothu, Abhishek. M.S., Department of Computer Science and Engineering, Wright State Uni- versity, 2019. Abusive and Hate Speech Tweets Detection with Text Generation. According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online harassment detection is vital for securing and sustaining the popularity and viability of online social networks. Machine learning techniques play a crucial role in automatic harassment detection. One of the challenges of using supervised approaches is training data imbalance. Existing text generation techniques can help augment the training data, but they are still inadequate and ineffective. This research explores the role of domain-specific knowledge to complement the limited training data available for training a text generator.
    [Show full text]
  • Learning Sentence Embeddings Using Recursive Networks
    Learning sentence embeddings using Recursive Networks Anson Bastos Independent scholar Mumbai, India [email protected] Abstract—Learning sentence vectors that generalise well is a II. SYSTEM ARCHITECTURE challenging task. In this paper we compare three methods of learning phrase embeddings: 1) Using LSTMs, 2) using recursive We have made use of three architectures as described below nets, 3) A variant of the method 2 using the POS information of the phrase. We train our models on dictionary definitions of words to obtain a reverse dictionary application similar to Felix A. LSTM et al. [1]. To see if our embeddings can be transferred to a new task we also train and test on the rotten tomatoes dataset [2]. We make use of the basic LSTM architecture as proposed We train keeping the sentence embeddings fixed as well as with by Zaremba et al. [9]. The equations describing the network fine tuning. is as follows: I. INTRODUCTION l−1 l Many methods to obtain sentence embeddings have been i = σ(Wi1 ∗ ht + Wi2 ∗ ht−1 + Bi) (1) researched in the past. Some of these include Hamid at al. l−1 l [3]. who have used LSTMs and Ozan et al. [4] who have f = σ(Wf1 ∗ ht + Wf2 ∗ ht−1 + Bf (2) made use of the recursive networks. Another approach used l−1 l by Mikolov et al. [4] is to include the paragraph in the context o = σ(Wo1 ∗ ht + Wg2 ∗ ht−1 + Bg) (3) of the target word and then use gradient descent at inference l−1 2 l time to find a new paragraphs embedding.
    [Show full text]
  • Learning Representations of Social Media Users Arxiv:1812.00436V1
    Learning Representations of Social Media Users by Adrian Benton A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland arXiv:1812.00436v1 [cs.LG] 2 Dec 2018 October, 2018 c 2018 by Adrian Benton ⃝ All rights reserved Abstract Social media users routinely interact by posting text updates, sharing images and videos, and establishing connections with other users through friending. User rep- resentations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation – effective for many downstream tasks – from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in arecom- mendation system) but there are also proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person’s demographic features, socio-economic class, or mental health state? Is it predictive of the user’s future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit.
    [Show full text]
  • Learning Bilingual Sentence Embeddings Via Autoencoding and Computing Similarities with a Multilayer Perceptron
    Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron Yunsu Kim2 Hendrik Rosendahl1;2 Nick Rossenbach2 Jan Rosendahl2 Shahram Khadivi1 Hermann Ney2 1eBay, Inc., Aachen, Germany fhrosendahl,[email protected] 2RWTH Aachen University, Aachen, Germany [email protected] Abstract 2011; Xu and Koehn, 2017). Most of the partici- pants in the WMT 2018 parallel corpus filtering We propose a novel model architecture and task use large-scale neural MT models and lan- training algorithm to learn bilingual sentence embeddings from a combination of parallel guage models as the features (Koehn et al., 2018). and monolingual data. Our method connects Bilingual sentence embeddings can be an ele- autoencoding and neural machine translation gant and unified solution for parallel corpus min- to force the source and target sentence embed- ing and filtering. They compress the information dings to share the same space without the help of each sentence into a single vector, which lies of a pivot language or an additional trans- in a shared space between source and target lan- formation. We train a multilayer perceptron guages. Scoring a source-target sentence pair is on top of the sentence embeddings to extract good bilingual sentence pairs from nonparallel done by computing similarity between the source or noisy parallel data. Our approach shows embedding vector and the target embedding vec- promising performance on sentence align- tor. It is much more efficient than scoring by de- ment recovery and the WMT 2018 parallel coding, e.g. with a translation model. corpus filtering tasks with only a single model.
    [Show full text]
  • Dependency-Based Convolutional Neural Networks for Sentence Embedding
    Dependency-based Convolutional Neural Networks for Sentence Embedding ROOT What is Hawaii ’s state flower ? Mingbo Ma Liang Huang Bing Xiang Bowen Zhou CUNY=>OSU IBM T. J. Watson ACL 2015 Beijing Convolutional Neural Network for Vision “deep learning” Layer 1 16,000 CPU cores (on 1,000 machines) => 12 GPUs (on 3 machines) Google/Stanford (2012) Stanford/NVIDIA (2013) 2 Convolutional Neural Network for Go “deep learning” Google (Silver et al., Nature 2016) and Facebook (Tian and Zhu, ICLR 2016) 48 CPUs and 8 GPUs 16 CPUs and 4 GPUs 3 Convolutional Neural Network for Language Kalchbrenner et al. (2014) and Kim (2014) apply CNNs to sentence modeling • alleviates data sparsity by word embedding • sequential order (sentence) instead of spatial order (image) Should use more linguistic and structural information! (implementations based on Python-based deep learning toolkit “Theano”) 4 Sequential Convolution Sequential convolution What is Hawaii ’s state flower 1 2 3 4 5 6 word rep. convolution direction 5 Sequential Convolution Sequential convolution What is Hawaii ’s state flower 1 2 3 4 5 6 word rep. convolution direction 6 Sequential Convolution Sequential convolution What is Hawaii ’s state flower 1 2 3 4 5 6 word rep. convolution direction 7 Sequential Convolution Sequential convolution What is Hawaii ’s state flower 1 2 3 4 5 6 word rep. convolution direction 8 Sequential Convolution Sequential convolution What is Hawaii ’s state flower 1 2 3 4 5 6 word rep. convolution direction 9 Try different convolution filters and repeat the same process 10 Sequential Convolution Sequential convolution What is Hawaii ’s state flower 1 2 3 4 5 6 word rep.
    [Show full text]
  • Automatic Text Summarization Using Reinforcement Learning with Embedding Features
    Automatic Text Summarization Using Reinforcement Learning with Embedding Features Gyoung Ho Lee, Kong Joo Lee Information Retrieval & Knowledge Engineering Lab., Chungnam National University, Daejeon, Korea [email protected], [email protected] Abstract so they are not applicable for text summarization tasks. An automatic text summarization sys- A Reinforcement Learning method would be an tem can automatically generate a short alternative approach to optimize a score function and brief summary that contains a in extractive text summarization task. Reinforce- main concept of an original document. ment learning is to learn how an agent in an envi- In this work, we explore the ad- ronment behaves in order to receive optimal re- vantages of simple embedding features wards in a given current state (Sutton, 1998). in Reinforcement leaning approach to The system learns the optimal policy that can automatic text summarization tasks. In choose a next action with the most reward value addition, we propose a novel deep in a given state. That is to say, the system can learning network for estimating Q- evaluate the quality of a partial summary and de- values used in Reinforcement learning. termine the sentence to insert in the summary to We evaluate our model by using get the most reward. It can produce a summary by ROUGE scores with DUC 2001, 2002, inserting a sentence one by one with considering Wikipedia, ACL-ARC data. Evalua- the quality of the hypothetical summary. In this tion results show that our model is work, we propose an extractive text summariza- competitive with the previous models. tion model for a single document based on a RL method.
    [Show full text]
  • Dependency-Based Convolutional Neural Networks for Sentence Embedding∗
    Dependency-based Convolutional Neural Networks for Sentence Embedding∗ Mingbo Ma† Liang Huang†‡ Bing Xiang‡ Bowen Zhou‡ †Graduate Center & Queens College ‡IBM Watson Group City University of New York T. J. Watson Research Center, IBM mma2,lhuang gc.cuny.edu lhuang,bingxia,zhou @us.ibm.com { } { } Abstract Indeed, in the sentiment analysis literature, re- searchers have incorporated long-distance infor- In sentence modeling and classification, mation from syntactic parse trees, but the results convolutional neural network approaches are somewhat inconsistent: some reported small have recently achieved state-of-the-art re- improvements (Gamon, 2004; Matsumoto et al., sults, but all such efforts process word vec- 2005), while some otherwise (Dave et al., 2003; tors sequentially and neglect long-distance Kudo and Matsumoto, 2004). As a result, syn- dependencies. To combine deep learn- tactic features have yet to become popular in the ing with linguistic structures, we pro- sentiment analysis community. We suspect one pose a dependency-based convolution ap- of the reasons for this is data sparsity (according n proach, making use of tree-based -grams to our experiments, tree n-grams are significantly rather than surface ones, thus utlizing non- sparser than surface n-grams), but this problem local interactions between words. Our has largely been alleviated by the recent advances model improves sequential baselines on all in word embedding. Can we combine the advan- four sentiment and question classification tages of both worlds? tasks, and achieves the highest published So we propose a very simple dependency-based accuracy on TREC. convolutional neural networks (DCNNs). Our model is similar to Kim (2014), but while his se- 1 Introduction quential CNNs put a word in its sequential con- Convolutional neural networks (CNNs), originally text, ours considers a word and its parent, grand- invented in computer vision (LeCun et al., 1995), parent, great-grand-parent, and siblings on the de- has recently attracted much attention in natural pendency tree.
    [Show full text]
  • A Sentence Embedding Method by Dissecting BERT-Based Word Models Bin Wang, Student Member, IEEE, and C.-C
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2020 1 SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models Bin Wang, Student Member, IEEE, and C.-C. Jay Kuo, Fellow, IEEE Abstract—Sentence embedding is an important research topic intermediate layers encode the most transferable features, in natural language processing (NLP) since it can transfer representation from higher layers are more expressive in high- knowledge to downstream tasks. Meanwhile, a contextualized level semantic information. Thus, information fusion across word representation, called BERT, achieves the state-of-the-art performance in quite a few NLP tasks. Yet, it is an open problem layers has its potential to provide a stronger representation. to generate a high quality sentence representation from BERT- Furthermore, by conducting experiments on patterns of the based word models. It was shown in previous study that different isolated word representation across layers in deep models, we layers of BERT capture different linguistic properties. This allows observe the following property. Words of richer information in us to fuse information across layers to find better sentence a sentence have higher variation in their representations, while representations. In this work, we study the layer-wise pattern of the word representation of deep contextualized models. Then, the token representation changes gradually, across layers. This we propose a new sentence embedding method by dissecting finding helps define “salient” word representations and infor- BERT-based word models through geometric analysis of the mative words in computing universal sentence embedding. space spanned by the word representation. It is called the One limitation of BERT is that due to the large model size, it SBERT-WK method1.
    [Show full text]