Leveraging Grammar Induction for Language Understanding and Generation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing grammar induction methods do not provide sufficient performance in downstream tasks. |
| Approach: | They propose an unsupervised grammar induction method for language understanding and generation using a grammar parser and a syntactic mask. |
| Outcome: | The proposed method performs better on from-scratch and pre-trained scenarios. |
Similar Papers
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models that induce grammar structures from data focus on constituency or dependency structures alone. |
| Approach: | They propose a model that can induce dependency and constituency structure at the same time. |
| Outcome: | The proposed model can induce both constituency and dependency structures at the same time. |
Rule Augmented Unsupervised Constituency Parsing (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that unsupervised parsing methods do not learn meaningful semantics (not even simple grammar) |
| Approach: | They propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic grammar rules and is independent of the base system. |
| Outcome: | The proposed model is independent of the base system and takes advantage of syntactic grammar rules. |
A Regularization-based Framework for Bilingual Grammar Induction (D19-1)
Copied to clipboard
| Challenge: | Existing multilingual grammar induction methods require external resources such as parallel corpora, word alignments or linguistic phylogenetic trees. |
| Approach: | They propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language. |
| Outcome: | The proposed method outperforms baselines on transfer grammar induction and bilingual grammar inducing on multiple languages. |
Grammar Induction with Neural Language Models: An Unusual Replication (D18-1)
Copied to clipboard
| 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. |
Video-aided Unsupervised Grammar Induction (2021.naacl-main)
Copied to clipboard
| 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. |
Categorial grammar induction from raw data (2023.findings-acl)
Copied to clipboard
| Challenge: | a new model for categorial grammar induction is based on raw data without part-of-speech information. |
| Approach: | They propose a grammar induction model that learns from raw data without part-of-speech information. |
| Outcome: | a new model for inducing a basic categorial grammar is developed . the model attains a recall-homogeneity of 0.33 on average, and a bias toward forward function application is added . |
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for predicting linguistic structures require labeled data . unsupervised chunking is useful for understanding linguistic structure of human languages . |
| Approach: | They propose a knowledge-transfer approach that heuristically induces chunk labels from unsupervised parsing models and a hierarchical recurrent neural network (HRNN) they show that their approach bridges the gap between supervised and unsupervised chunking. |
| Outcome: | The proposed method bridges the gap between supervised and unsupervised chunking. |
On Eliciting Syntax from Language Models via Hashing (2024.emnlp-main)
Copied to clipboard
| 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. |
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages (2021.findings-emnlp)
Copied to clipboard
| 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. |
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale (2022.tacl-1)
Copied to clipboard
| Challenge: | a novel class of Transformer language models that combine expressive power, scalability, and strong performance of Transformers and recursive syntactic compositions. |
| Approach: | They introduce Transformer Grammars, a class of Transformer language models that combine expressive power and recursive syntactic compositions. |
| Outcome: | The proposed model outperforms strong baselines on sentence-level language modeling perplexity and syntax-sensitive language evaluation metrics. |