| 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. |
Similar Papers
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
Copied to clipboard
| Challenge: | a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
| Outcome: | The proposed model outperforms state-of-the-art models under similar conditions. |
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. |
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
Copied to clipboard
| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
| Approach: | They propose to use syntactic information to integrate syntastic structure into neural language models by providing ground truth parse trees as additional training signals. |
| Outcome: | The proposed model achieves lower perplexity and better quality when ground truth parse trees are provided as training signals. |
Unsupervised Recurrent Neural Network Grammars (N19-1)
Copied to clipboard
| Challenge: | RNNGs model syntax and structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. |
| Approach: | They explore unsupervised learning of recurrent neural network grammars for language modeling and grammar induction. |
| Outcome: | The proposed model outperforms standard sequential language models and improves parsing performance. |
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. |
ListOps: A Diagnostic Dataset for Latent Tree Learning (N18-4)
Copied to clipboard
| Challenge: | Existing work on latent tree learning models shows they do not learn plausible grammars . a dataset is created to study the parsing ability of such models in natural language . |
| Approach: | They propose a toy dataset to study the parsing ability of latent tree learning models . they propose 'listops' toy that has a single correct parse strategy that a system needs to learn . |
| Outcome: | The proposed model outperforms existing models on sentence understanding tasks . it can learn grammars that conform to plausible semantics and syntactic formalisms . |
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 . |
A Tree-based Decoder for Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
| Approach: | They propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side. |
| Outcome: | The proposed model outperforms standard seq2seq models by 2.1 BLEU points and other methods for incorporating target-side syntax by 0.7 BLUE points. |
The Return of Lexical Dependencies: Neural Lexicalized PCFGs (2020.tacl-1)
Copied to clipboard
| 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. |