| 41 |
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?
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In: Computational Linguistics, Vol 46, Iss 4, Pp 763-784 (2021) (2021)
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Abstract:
AbstractThere is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hierarchical structure. We present a linguistic investigation of a neural parser adding insights to this question. We look at transitivity and agreement information of auxiliary verb constructions (AVCs) in comparison to finite main verbs (FMVs). This comparison is motivated by theoretical work in dependency grammar and in particular the work of Tesnière (1959), where AVCs and FMVs are both instances of a nucleus, the basic unit of syntax. An AVC is a dissociated nucleus; it consists of at least two words, and an FMV is its non-dissociated counterpart, consisting of exactly one word. We suggest that the representation of AVCs and FMVs should capture similar information. We use diagnostic classifiers to probe agreement and transitivity information in vectors learned by a transition-based neural parser in four typologically different languages. We find that the parser learns different information about AVCs and FMVs if only sequential models (BiLSTMs) are used in the architecture but similar information when a recursive layer is used. We find explanations for why this is the case by looking closely at how information is learned in the network and looking at what happens with different dependency representations of AVCs. We conclude that there may be benefits to using a recursive layer in dependency parsing and that we have not yet found the best way to integrate it in our parsers.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doi.org/10.1162/coli_a_00392
https://doaj.org/article/7b4d6e78cc6e49f294e0664fb59690c8
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| 42 |
Efficient Computation of Expectations under Spanning Tree Distributions
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 675-690 (2021) (2021)
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| 43 |
Revisiting Multi-Domain Machine Translation
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 17-35 (2021) (2021)
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| 44 |
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
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In: Computational Linguistics, Vol 47, Iss 1, Pp 117-140 (2021) (2021)
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| 45 |
Semantic Data Set Construction from Human Clustering and Spatial Arrangement
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In: Computational Linguistics, Vol 47, Iss 1, Pp 69-116 (2021) (2021)
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| 46 |
WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 211-225 (2021) (2021)
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| 47 |
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 195-210 (2021) (2021)
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| 48 |
Aligning Faithful Interpretations with their Social Attribution
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 294-310 (2021) (2021)
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| 49 |
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1408-1424 (2021) (2021)
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| 50 |
Evaluating Document Coherence Modeling
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 621-640 (2021) (2021)
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| 51 |
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing
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In: Computational Linguistics, Vol 47, Iss 1, Pp 43-68 (2021) (2021)
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| 52 |
Model Compression for Domain Adaptation through Causal Effect Estimation
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1355-1373 (2021) (2021)
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| 53 |
Planning with Learned Entity Prompts for Abstractive Summarization
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1475-1492 (2021) (2021)
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| 54 |
Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 243-260 (2021) (2021)
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| 55 |
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1285-1302 (2021) (2021)
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| 56 |
Supervised and Unsupervised Neural Approaches to Text Readability
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In: Computational Linguistics, Vol 47, Iss 1, Pp 141-179 (2021) (2021)
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| 57 |
A Graph-Based Framework for Structured Prediction Tasks in Sanskrit
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In: Computational Linguistics, Vol 46, Iss 4, Pp 785-845 (2021) (2021)
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| 58 |
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1163-1179 (2021) (2021)
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| 59 |
Efficient Outside Computation
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In: Computational Linguistics, Vol 46, Iss 4, Pp 745-762 (2021) (2021)
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| 60 |
There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 605-620 (2021) (2021)
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