DOCSLIB.ORG

Generating Lexical Representations of Frames Using Lexical Substitution Saba Anwar†, Artem Shelmanov‡, Alexander Panchenko‡, and Chris Biemann†

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Generating Lexical Representations of Frames Using Lexical Substitution Saba Anwar†, Artem Shelmanov‡, Alexander Panchenko‡, and Chris Biemann† Generating Lexical Representations of Frames using Lexical Substitution Saba Anwary, Artem Shelmanovz, Alexander Panchenkoz, and Chris Biemanny yUniversitat¨ Hamburg, Germany zSkolkovo Institute of Science and Technology, Russia fanwar,[email protected] a.fshelmanov,[email protected] Abstract Seed sentence: I hope PattiHelper can helpAssistance youBenefited party soonTime . Semantic frames are formal linguistic struc- Substitutes for Assistance: assist, aid tures describing situations/actions/events, e.g. Substitutes for Helper: she, I, he, you, we, someone, Commercial transfer of goods. Each frame they, it, lori, hannah, paul, sarah, melanie, pam, riley provides a set of roles corresponding to the sit- Substitutes for Benefited party: me, him, folk, her, uation participants, e.g. Buyer and Goods, and everyone, people Substitutes for Time: tomorrow, now, shortly, sooner, lexical units (LUs) – words and phrases that tonight, today, later can evoke this particular frame in texts, e.g. Sell. The scarcity of annotated resources hin- Table 1: An example of the induced lexical represen- ders wider adoption of frame semantics across tation (roles and LUs) of the Assistance FrameNet languages and domains. We investigate a sim- frame using lexical substitutes from a single seed sen- ple yet effective method, lexical substitution tence. with word representation models, to automat- ically expand a small set of frame-annotated lenging and requires semanticists or very complex sentences with new words for their respective crowd-sourcing setups (Fossati et al., 2013). roles and LUs. We evaluate the expansion quality using FrameNet. Contextualized mod- We suggest a different perspective on the prob- els demonstrate overall superior performance lem: expanding the FrameNet resource automati- compared to the non-contextualized ones on cally by using lexical substitution. Given a small roles. However, the latter show comparable set of seed sentences with their frame annotations, performance on the task of LU expansion. we can expand it by substituting the targets (words corresponding to lexical units of the respective 1 Introduction frame) and arguments (words corresponding to The goal of lexical substitution (McCarthy and roles of the respective frame) of those sentences Navigli, 2009) is to replace a given target word and aggregating possible substitutions into an in- in its context with meaning-preserving alterna- duced frame-semantic resource. Table1 shows tives. In this paper, we show how lexical sub- one such induced example. For this purpose, stitution can be used for semantic frame expan- we have experimented with state-of-the-art non- sion. A semantic frame is a linguistic structure contextualized (static) word representation mod- used to describe the formal meaning of a situa- els including neural word embeddings, i.e. fast- tion or event (Fillmore, 1982). Semantic frames Text (Bojanowski et al., 2017), GloVe (Pennington have witnessed a wide range of applications; such et al., 2014), and word2vec (Mikolov et al., 2013); as question answering (Shen and Lapata, 2007; and distributional thesaurus, i.e. JoBimText (Bie- Berant and Liang, 2014; Khashabi et al., 2018), mann and Riedl, 2013); and compared their results machine translation (Gao and Vogel, 2011; Zhai with contextualized word representations of the et al., 2013), and semantic role labelling (Do et al., state-of-the-art BERT model (Devlin et al., 2019), 2017; Swayamdipta et al., 2018). The impact, which has set a new benchmark performance on however, is limited by the scarce availability of many downstream NLP applications. To complete annotated resources. Some publicly available re- the comparison, we also include the lexical substi- sources are FrameNet (Baker et al., 1998) and tution model of Melamud et al.(2015), which uses PropBank (Palmer et al., 2005), yet for many lan- dependency-based word and context embeddings guages and domains, specialized resources do not and produces context-sensitive lexical substitutes. exist. Besides, due to the inherent vagueness To generate substitutes, we decompose the of frame definitions, the annotation task is chal- problem into two sub-tasks: Lexical unit expan- sion: Given a sentence and its target word, the task places ELMo with BERT (Devlin et al., 2019) for is to generate frame preserving substitutes for this improved performance. Zhou et al.(2019) show word. Frame role expansion: Given a sentence the utility of BERT for the lexical substitution and an argument, the task is to generate meaning- task. Lexical substitution has been used for a preserving substitutes for this argument. range of NLP tasks such as paraphrasing or text Contributions of our work are (i) a method for simplification, but here, we are employing it, as inducing frame-semantic resources based on a few far as we are aware, for the first time to perform frame-annotated sentences using lexical substitu- expansion of frame-semantic resources. tion, and (ii) an evaluation of various distribu- tional semantic models and lexical substitution 3 Inducing Lexical Representations of methods on the ground truth from FrameNet. Frames via Lexical Substitution We experimented with two groups of lexical sub- 2 Related Work stitution methods. The first one use no con- Approaches to semantic frame parsing with re- text: non-contextualized neural word embed- spect to a pre-defined semantic frame resource, ding models, i.e. fastText (Bojanowski et al., such as FrameNet, have received much atten- 2017), GloVe (Pennington et al., 2014), and tion in the literature (Das et al., 2010; Oepen word2vec (Mikolov et al., 2013), as well as dis- et al., 2016; Yang and Mitchell, 2017; Peng et al., tributional thesaurus based models in the form of 2018), with SEMAFOR (Das et al., 2014) be- JoBimText (Biemann and Riedl, 2013). The sec- ing a most widely known system to extract com- ond group of methods does use the context: here, plete frame structure including target identifica- we tried contextualized word embedding model tion. Some works focus on identifying par- BERT (Devlin et al., 2019) and the lexical substi- tial structures such as frame identification (Hart- tution model of Melamud et al.(2015). mann et al., 2017; Hermann et al., 2014), role 3.1 Static Word Representations labelling with frame identification (Swayamdipta et al., 2017; Yang and Mitchell, 2017), and sim- These word representations models are inherently ple role labelling (Kshirsagar et al., 2015; Roth non-contextualized as they learn one representa- and Lapata, 2015; Swayamdipta et al., 2018), tion of a word regardless of its context. which is considered very similar to standard Prop- Neural Word Embeddings Neural word em- Bank (Palmer et al., 2005) style semantic role la- beddings represent words as vectors of continu- belling, albeit more challenging because of the ous numbers, where words with similar meanings high granularity of frame roles. These super- are expected to have similar vectors. Thus, to pro- vised models rely on a dataset of frame-annotated duce substitutes, we extracted the k nearest neigh- sentences such as FrameNet. FrameNet-like re- bors using a cosine similarity measure. We use sources are available only for very few languages pre-trained embeddings by authors models: fast- and cover only a few domains. In this paper, we Text trained on the Common Crawl corpus, GloVe venture into the inverse problem, the case where trained on Common Crawl corpus with 840 billion the number of annotations is insufficient, simi- words, word2vec trained on Google News. All lar to the idea of Pennacchiotti et al.(2008) who these models produce 300-dimension vectors. investigated the utility of semantic spaces and WordNet-based methods to automatically induce Distributional Thesaurus (DT) In this ap- new LUs and reported their results on FrameNet. proach, word similarities are computed using com- Our method is inspired by the recent work of plex linguistic features such as dependency rela- Amrami and Goldberg(2018). They suggest to tions (Lin, 1998). The representations provided predict the substitutes vectors for target words us- by DTs are sparser, but similarity scores based ing pre-trained ELMo (Peters et al., 2018) and dy- on them can be better. JoBimText (Biemann and namic symmetric patterns, then induced the word Riedl, 2013) is a framework that offers many DTs senses using clustering. Arefyev et al.(2019) takes computed on a range of different corpora. Context the idea of substitute vectors from (Amrami and features for each word are ranked using the lexi- Goldberg, 2018) for the SemEval 2019 (Qasem- cographer’s mutual information (LMI) score and iZadeh et al., 2019) frame induction task and re- used to compute word similarity by feature over- lap. We extract the k nearest neighbors for the tar- roles and LUs can consist of a single token or mul- get word. We use two JoBimText DTs: (i) DT tiple tokens. For this work, we have only con- built on Wikipedia with n-grams as contexts and sidered a single-token substitution. The datasets (ii) DT built on a 59G corpus (Wikipedia, Giga- for evaluation were derived automatically from word, ukWaC, and LCC corpora combined) using FrameNet. To create a gold standard for LU ex- dependency relations as context. pansion task, for each sentence containing an an- notated LU, we consider other LUs of the cor- 3.2 Contextualized Models responding semantic frame as ground truth sub- Static word representations fail to handle poly- stitutes. We keep only LUs marked as verbs in semic words. This paves the way for context- FrameNet. To make a gold standard for the role aware word representation models, which can gen- expansion task, for each of the sentences that con- erate diverse word-probability distributions for a tain an annotation of a given frame role, we con- target word based on its context. sider all the single-word annotations from the rest of the corpus marked with the same role and re- Melamud et al.(2015) This simple model uses lated to the same frame as ground truth substitutes.
Load more

Recommended publications
  • Malware Classification with BERT
    San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family.
    [Show full text]
  • Bros:Apre-Trained Language Model for Understanding Textsin Document
    Under review as a conference paper at ICLR 2021 BROS: A PRE-TRAINED LANGUAGE MODEL FOR UNDERSTANDING TEXTS IN DOCUMENT Anonymous authors Paper under double-blind review ABSTRACT Understanding document from their visual snapshots is an emerging and chal- lenging problem that requires both advanced computer vision and NLP methods. Although the recent advance in OCR enables the accurate extraction of text seg- ments, it is still challenging to extract key information from documents due to the diversity of layouts. To compensate for the difficulties, this paper introduces a pre-trained language model, BERT Relying On Spatiality (BROS), that represents and understands the semantics of spatially distributed texts. Different from pre- vious pre-training methods on 1D text, BROS is pre-trained on large-scale semi- structured documents with a novel area-masking strategy while efficiently includ- ing the spatial layout information of input documents. Also, to generate structured outputs in various document understanding tasks, BROS utilizes a powerful graph- based decoder that can capture the relation between text segments. BROS achieves state-of-the-art results on four benchmark tasks: FUNSD, SROIE*, CORD, and SciTSR. Our experimental settings and implementation codes will be publicly available. 1 INTRODUCTION Document intelligence (DI)1, which understands industrial documents from their visual appearance, is a critical application of AI in business. One of the important challenges of DI is a key information extraction task (KIE) (Huang et al., 2019; Jaume et al., 2019; Park et al., 2019) that extracts struc- tured information from documents such as financial reports, invoices, business emails, insurance quotes, and many others.
    [Show full text]
  • Information Extraction Based on Named Entity for Tourism Corpus
    Information Extraction based on Named Entity for Tourism Corpus Chantana Chantrapornchai Aphisit Tunsakul Dept. of Computer Engineering Dept. of Computer Engineering Faculty of Engineering Faculty of Engineering Kasetsart University Kasetsart University Bangkok, Thailand Bangkok, Thailand [email protected] [email protected] Abstract— Tourism information is scattered around nowa- The ontology is extracted based on HTML web structure, days. To search for the information, it is usually time consuming and the corpus is based on WordNet. For these approaches, to browse through the results from search engine, select and the time consuming process is the annotation which is to view the details of each accommodation. In this paper, we present a methodology to extract particular information from annotate the type of name entity. In this paper, we target at full text returned from the search engine to facilitate the users. the tourism domain, and aim to extract particular information Then, the users can specifically look to the desired relevant helping for ontology data acquisition. information. The approach can be used for the same task in We present the framework for the given named entity ex- other domains. The main steps are 1) building training data traction. Starting from the web information scraping process, and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used the data are selected based on the HTML tag for corpus to train for creating vocabulary embedding. Also, it is used building. The data is used for model creation for automatic for creating annotated data.
    [Show full text]
  • NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and Dlpy Doug Cairns and Xiangxiang Meng, SAS Institute Inc
    Paper SAS4429-2020 NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and DLPy Doug Cairns and Xiangxiang Meng, SAS Institute Inc. ABSTRACT A revolution is taking place in natural language processing (NLP) as a result of two ideas. The first idea is that pretraining a deep neural network as a language model is a good starting point for a range of NLP tasks. These networks can be augmented (layers can be added or dropped) and then fine-tuned with transfer learning for specific NLP tasks. The second idea involves a paradigm shift away from traditional recurrent neural networks (RNNs) and toward deep neural networks based on Transformer building blocks. One architecture that embodies these ideas is Bidirectional Encoder Representations from Transformers (BERT). BERT and its variants have been at or near the top of the leaderboard for many traditional NLP tasks, such as the general language understanding evaluation (GLUE) benchmarks. This paper provides an overview of BERT and shows how you can create your own BERT model by using SAS® Deep Learning and the SAS DLPy Python package. It illustrates the effectiveness of BERT by performing sentiment analysis on unstructured product reviews submitted to Amazon. INTRODUCTION Providing a computer-based analog for the conceptual and syntactic processing that occurs in the human brain for spoken or written communication has proven extremely challenging. As a simple example, consider the abstract for this (or any) technical paper. If well written, it should be a concise summary of what you will learn from reading the paper. As a reader, you expect to see some or all of the following: • Technical context and/or problem • Key contribution(s) • Salient result(s) If you were tasked to create a computer-based tool for summarizing papers, how would you translate your expectations as a reader into an implementable algorithm? This is the type of problem that the field of natural language processing (NLP) addresses.
    [Show full text]
  • Enriching an Explanatory Dictionary with Framenet and Propbank Corpus Examples
    Proceedings of eLex 2019 Enriching an Explanatory Dictionary with FrameNet and PropBank Corpus Examples Pēteris Paikens 1, Normunds Grūzītis 2, Laura Rituma 2, Gunta Nešpore 2, Viktors Lipskis 2, Lauma Pretkalniņa2, Andrejs Spektors 2 1 Latvian Information Agency LETA, Marijas street 2, Riga, Latvia 2 Institute of Mathematics and Computer Science, University of Latvia, Raina blvd. 29, Riga, Latvia E-mail: [email protected], [email protected] Abstract This paper describes ongoing work to extend an online dictionary of Latvian – Tezaurs.lv – with representative semantically annotated corpus examples according to the FrameNet and PropBank methodologies and word sense inventories. Tezaurs.lv is one of the largest open lexical resources for Latvian, combining information from more than 300 legacy dictionaries and other sources. The corpus examples are extracted from Latvian FrameNet and PropBank corpora, which are manually annotated parallel subsets of a balanced text corpus of contemporary Latvian. The proposed approach augments traditional lexicographic information with modern cross-lingually interpretable information and enables analysis of word senses from the perspective of frame semantics, which is substantially different from (complementary to) the traditional approach applied in Latvian lexicography. In cases where FrameNet and PropBank corpus evidence aligns well with the word sense split in legacy dictionaries, the frame-semantically annotated corpus examples supplement the word sense information with clarifying usage examples and commonly used semantic valence patterns. However, the annotated corpus examples often provide evidence of a different sense split, which is often more coarse-grained and, thus, may help dictionary users to cluster and comprehend a fine-grained sense split suggested by the legacy sources.
    [Show full text]
  • Unified Language Model Pre-Training for Natural
    Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1].
    [Show full text]
  • Question Answering by Bert
    Running head: QUESTION AND ANSWERING USING BERT 1 Question and Answering Using BERT Suman Karanjit, Computer SCience Major Minnesota State University Moorhead Link to GitHub https://github.com/skaranjit/BertQA QUESTION AND ANSWERING USING BERT 2 Table of Contents ABSTRACT .................................................................................................................................................... 3 INTRODUCTION .......................................................................................................................................... 4 SQUAD ............................................................................................................................................................ 5 BERT EXPLAINED ...................................................................................................................................... 5 WHAT IS BERT? .......................................................................................................................................... 5 ARCHITECTURE ............................................................................................................................................ 5 INPUT PROCESSING ...................................................................................................................................... 6 GETTING ANSWER ........................................................................................................................................ 8 SETTING UP THE ENVIRONMENT. ....................................................................................................
    [Show full text]
  • Developing a Large Scale Framenet for Italian: the Iframenet Experience
    Developing a large scale FrameNet for Italian: the IFrameNet experience Roberto Basili° Silvia Brambilla§ Danilo Croce° Fabio Tamburini§ ° § Dept. of Enterprise Engineering Dept. of Classic Philology and Italian Studies University of Rome Tor Vergata University of Bologna {basili,croce}@info.uniroma2.it [email protected], [email protected] ian Portuguese, German, Spanish, Japanese, Swe- Abstract dish and Korean. All these projects are based on the idea that English. This paper presents work in pro- most of the Frames are the same among languages gress for the development of IFrameNet, a and that, thanks to this, it is possible to adopt large-scale, computationally oriented, lexi- Berkeley’s Frames and FEs and their relations, cal resource based on Fillmore’s frame se- with few changes, once all the language-specific mantics for Italian. For the development of information has been cut away (Tonelli et al. 2009, IFrameNet linguistic analysis, corpus- Tonelli 2010). processing and machine learning techniques With regard to Italian, over the past ten years are combined in order to support the semi- several research projects have been carried out at automatic development and annotation of different universities and Research Centres. In par- the resource. ticular, the ILC-CNR in Pisa (e.g. Lenci et al. 2008; Johnson and Lenci 2011), FBK in Trento (e.g. Italiano. Questo articolo presenta un work Tonelli et al. 2009, Tonelli 2010) and the Universi- in progress per lo sviluppo di IFrameNet, ty of Rome, Tor Vergata (e.g. Pennacchiotti et al. una risorsa lessicale ad ampia copertura, 2008, Basili et al.
    [Show full text]
  • Distilling BERT for Natural Language Understanding
    TinyBERT: Distilling BERT for Natural Language Understanding Xiaoqi Jiao1∗,y Yichun Yin2∗z, Lifeng Shang2z, Xin Jiang2 Xiao Chen2, Linlin Li3, Fang Wang1z and Qun Liu2 1Key Laboratory of Information Storage System, Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics 2Huawei Noah’s Ark Lab 3Huawei Technologies Co., Ltd. fjiaoxiaoqi,[email protected] fyinyichun,shang.lifeng,[email protected] fchen.xiao2,lynn.lilinlin,[email protected] Abstract natural language processing (NLP). Pre-trained lan- guage models (PLMs), such as BERT (Devlin et al., Language model pre-training, such as BERT, has significantly improved the performances 2019), XLNet (Yang et al., 2019), RoBERTa (Liu of many natural language processing tasks. et al., 2019), ALBERT (Lan et al., 2020), T5 (Raf- However, pre-trained language models are usu- fel et al., 2019) and ELECTRA (Clark et al., 2020), ally computationally expensive, so it is diffi- have achieved great success in many NLP tasks cult to efficiently execute them on resource- (e.g., the GLUE benchmark (Wang et al., 2018) restricted devices. To accelerate inference and the challenging multi-hop reasoning task (Ding and reduce model size while maintaining et al., 2019)). However, PLMs usually have a accuracy, we first propose a novel Trans- former distillation method that is specially de- large number of parameters and take long infer- signed for knowledge distillation (KD) of the ence time, which are difficult to be deployed on Transformer-based models. By leveraging this edge devices such as mobile phones. Recent stud- new KD method, the plenty of knowledge en- ies (Kovaleva et al., 2019; Michel et al., 2019; Voita coded in a large “teacher” BERT can be ef- et al., 2019) demonstrate that there is redundancy fectively transferred to a small “student” Tiny- in PLMs.
    [Show full text]
  • Integrating Wordnet and Framenet Using a Knowledge-Based Word Sense Disambiguation Algorithm
    Integrating WordNet and FrameNet using a knowledge-based Word Sense Disambiguation algorithm Egoitz Laparra and German Rigau IXA group. University of the Basque Country, Donostia {egoitz.laparra,german.rigau}@ehu.com Abstract coherent groupings of words belonging to the same frame. This paper presents a novel automatic approach to In that way we expect to extend the coverage of FrameNet partially integrate FrameNet and WordNet. In that (by including from WordNet closely related concepts), to way we expect to extend FrameNet coverage, to en- enrich WordNet with frame semantic information (by port- rich WordNet with frame semantic information and ing frame information to WordNet) and possibly to extend possibly to extend FrameNet to languages other than FrameNet to languages other than English (by exploiting English. The method uses a knowledge-based Word local wordnets aligned to the English WordNet). Sense Disambiguation algorithm for linking FrameNet WordNet1 [12] (hereinafter WN) is by far the most lexical units to WordNet synsets. Specifically, we ex- ploit a graph-based Word Sense Disambiguation algo- widely-used knowledge base. In fact, WN is being rithm that uses a large-scale knowledge-base derived used world-wide for anchoring different types of seman- from WordNet. We have developed and tested four tic knowledge including wordnets for languages other than additional versions of this algorithm showing a sub- English [4], domain knowledge [17] or ontologies like stantial improvement over previous results. SUMO [22] or the EuroWordNet Top Concept Ontology [3]. It contains manually coded information about En- glish nouns, verbs, adjectives and adverbs and is organized around the notion of a synset.
    [Show full text]
  • Arxiv:2104.05274V2 [Cs.CL] 27 Aug 2021 NLP field Due to Its Excellent Performance in Various Tasks
    Learning to Remove: Towards Isotropic Pre-trained BERT Embedding Yuxin Liang1, Rui Cao1, Jie Zheng?1, Jie Ren2, and Ling Gao1 1 Northwest University, Xi'an, China fliangyuxin,[email protected], fjzheng,[email protected] 2 Shannxi Normal University, Xi'an, China [email protected] Abstract. Research in word representation shows that isotropic em- beddings can significantly improve performance on downstream tasks. However, we measure and analyze the geometry of pre-trained BERT embedding and find that it is far from isotropic. We find that the word vectors are not centered around the origin, and the average cosine similar- ity between two random words is much higher than zero, which indicates that the word vectors are distributed in a narrow cone and deteriorate the representation capacity of word embedding. We propose a simple, and yet effective method to fix this problem: remove several dominant directions of BERT embedding with a set of learnable weights. We train the weights on word similarity tasks and show that processed embed- ding is more isotropic. Our method is evaluated on three standardized tasks: word similarity, word analogy, and semantic textual similarity. In all tasks, the word embedding processed by our method consistently out- performs the original embedding (with average improvement of 13% on word analogy and 16% on semantic textual similarity) and two baseline methods. Our method is also proven to be more robust to changes of hyperparameter. Keywords: Natural language processing · Pre-trained embedding · Word representation · Anisotropic. 1 Introduction With the rise of Transformers [12], its derivative model BERT [1] stormed the arXiv:2104.05274v2 [cs.CL] 27 Aug 2021 NLP field due to its excellent performance in various tasks.
    [Show full text]
  • The Hebrew Framenet Project
    The Hebrew FrameNet Project Avi Hayoun, Michael Elhadad Dept. of Computer Science Ben-Gurion University Beer Sheva, Israel {hayounav,elhadad}@cs.bgu.ac.il Abstract We present the Hebrew FrameNet project, describe the development and annotation processes and enumerate the challenges we faced along the way. We have developed semi-automatic tools to help speed the annotation and data collection process. The resource currently covers 167 frames, 3,000 lexical units and about 500 fully annotated sentences. We have started training and testing automatic SRL tools on the seed data. Keywords: FrameNet, Hebrew, frame semantics, semantic resources 1. Introduction frames and their structures in natural language. Recent years have seen growing interest in the task 1.2. FrameNet in Other Languages of Semantic Role Labeling (SRL) of natural language The original FrameNet project has been adapted and text (sometimes called “shallow semantic parsing”). ported to multiple languages. The most active interna- The task is usually described as the act of identify- tional FrameNet teams include the Swedish FrameNet ing the semantic roles, which are the set of semantic (SweFN) covering close to 1,200 frames with 34K LUs properties and relationships defined over constituents (Ahlberg et al., 2014); the Japanese FrameNet (JFN) of a sentence, given a semantic context. with 565 frames, 8,500 LUs and 60K annotated ex- The creation of resources that document the realiza- ample sentences (Ohara, 2013); and FrameNet Brazil tion of semantic roles in natural language texts, such (FN-Br) covering 179 frames, 196 LUs and 12K anno- as FrameNet (Fillmore and Baker, 2010; Ruppenhofer tated sentences (Torrent and Ellsworth, 2013).
    [Show full text]