| Challenge: | Existing studies on empty category detection have shown positive effects on syntactic parsing . empty categories are used to indicate long-distance dependencies, discontinuous constituents, and certain dropped elements. |
| Approach: | They propose to use ECD to detect empty categories without syntactic analysis. |
| Outcome: | The proposed models outperform the prior state-of-the-art by significant margins. |
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. |
The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | a language model that is syntax-aware can produce better samples, authors say . a recent study shows that neural approaches to syntax can perform unsupervised syntactic parsing . |
| Approach: | They propose to incorporate syntax into neural approaches in NLP to produce better samples . they find that the first time neural approaches were able to perform unsupervised syntactic parsing . |
| Outcome: | The proposed models can perform unsupervised syntactic parsing, but they are lagging behind . the proposed models are based on a sandbox of probabilistic context-free-grammars . |
Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
Copied to clipboard
| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
| Approach: | This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse. |
| Outcome: | This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses. |
Two Local Models for Neural Constituent Parsing (C18-1)
Copied to clipboard
| Challenge: | Non-local features have been shown crucial for statistical parsing, but local models can give highly competitive accuracies thanks to the power of dense neural input representations. |
| Approach: | They propose to use local neural models for constituent parsing to capture dependencies between sub output structures and to exploit non-local features. |
| Outcome: | The proposed model achieves labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1. |
The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers (P19-1)
Copied to clipboard
| Challenge: | Existing non-neural dependency parsers benefit from information coming from structural features . however, their successors lack explicit information about the structural context . |
| Approach: | They propose to model the structural context of a biLSTM-based dependency parser with features built from partial subtrees. |
| Outcome: | The proposed models do not use any conventional structural features but capture implicitly the structural context. |
Lexicosyntactic Inference in Neural Models (D18-1)
Copied to clipboard
| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
| Outcome: | The proposed model makes systematic errors that are visible through the lens of factuality prediction. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
Copied to clipboard
| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
| Outcome: | The proposed method outperforms previous methods on diverse tasks. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
Copied to clipboard
| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |