Papers with grammar induction
The Return of Lexical Dependencies: Neural Lexicalized PCFGs (2020.tacl-1)
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| Challenge: | Existing approaches to grammar induction focus on discovering constituents or dependencies. |
| Approach: | They propose to model lexical dependencies using context free grammars instead of lexicals . they show that this unified framework induces both constituents and dependencies . |
| Outcome: | The proposed model overcomes sparsity problems and induces constituents and dependencies better than the current methods. |
Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction (2024.findings-naacl)
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Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Weinberger, Jitendra Malik, Trevor Darrell, Dan Klein
| Challenge: | Recent studies show multimodal inputs can improve grammar induction, but weak textual baselines are needed for training. |
| Approach: | They use a fixed grammar family to compare multimodal grammar induction methods . they find multimodal inputs can improve grammar induction by grounding textual inputs to the visual world . |
| Outcome: | The proposed model outperforms weaker baselines on four benchmark datasets. |
Video-aided Unsupervised Grammar Induction (2021.naacl-main)
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| Challenge: | Existing methods of multi-modal grammar induction focus on grammar inducing from text-image pairs, but videos provide even richer information, such as static objects and actions. |
| Approach: | They propose a video-aided grammar induction model which learns a constituency parser from unlabeled text and its corresponding video. |
| Outcome: | The proposed model outperforms existing systems on three benchmarks. |
Neural Bi-Lexicalized PCFG Induction (2021.acl-long)
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| Challenge: | Neural lexicalized PCFGs make strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. |
| Approach: | They propose an approach to parameterize L-PCFGs without making implausible independence assumptions. |
| Outcome: | The proposed approach improves both running speed and unsupervised parsing performance on the English WSJ dataset. |
Compound Probabilistic Context-Free Grammars for Grammar Induction (P19-1)
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| Challenge: | Existing approaches to grammar induction have resorted to manually-engineered features and auxiliary objectives to induce the desired structures. |
| Approach: | They propose a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context free grammar. |
| Outcome: | Experiments on English and Chinese show that the proposed approach is more efficient than other methods. |
Categorial Grammar Induction with Stochastic Category Selection (2024.lrec-main)
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| Challenge: | categorial grammar inducers have been used to learn from raw data, but they use shortcuts to ensure branching behavior. |
| Approach: | They propose a grammar inducer that learns from raw data and does not rely on bias terms . they show a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
| Outcome: | The proposed model achieves a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages (2021.findings-emnlp)
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| Challenge: | Existing models for syntactic acquisition are word-based and do not inspect functional affixes. |
| Approach: | They propose a computer-based induction model that allows a clean ablation of the influence of subword information in grammar induction. |
| Outcome: | The proposed model is more accurate in morphologically richer languages with subword information than word-based models. |
Mitigating Biases in Hate Speech Detection from A Causal Perspective (2023.findings-emnlp)
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| Challenge: | Existing methods to detect hate speech are prone to spurious correlations between training data and labels, which could lead to biased treatment of vulnerable and minority groups. |
| Approach: | They propose to use grammar induction to find grammar patterns for hate speech and analyze this phenomenon from a causal perspective. |
| Outcome: | The proposed methods can detect hate speech from a causal perspective and are robust to different datasets. |
On Eliciting Syntax from Language Models via Hashing (2024.emnlp-main)
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| Challenge: | Unsupervised parsing aims to infer syntactic structure from raw text . despite its importance, advancements in this task have been slow . |
| Approach: | They propose to use unsupervised parsing to infer syntactic structure from raw text . they upgrade the bit-level CKY to first-order to encode lexicon and syntax . |
| Outcome: | The proposed method shows competitive performance on various datasets. |
Grammar Induction with Neural Language Models: An Unusual Replication (D18-1)
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| Challenge: | Recent work on latent tree learning attempts to develop models with parse-valued latent variables and train them on non-parsing tasks. |
| Approach: | They propose a model with parse-valued latent variables and a strong latent tree learning result on constituency parsing. |
| Outcome: | The proposed model outperforms all baselines and performs competitively with symbolic grammar induction systems. |