| Challenge: | Syntactic scaffolds avoid expensive syntactical processing at runtime . many systems have used syntastic dependency or phrase-based parsers as preprocessing for semantic analysis. |
| Approach: | They propose a multitask learning approach that uses a syntactic treebank to integrate syntaktic information into semantic tasks. |
| Outcome: | The proposed method improves on PropBank semantics, frame semantics and coreference resolution tasks. |
Similar Papers
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. |
Improving Text Understanding via Deep Syntax-Semantics Communication (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance. |
| Approach: | They propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. |
| Outcome: | The proposed model outperforms baseline models on syntax-dependent tasks by a large margin. |
Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
| Outcome: | The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank. |
How does the brain process syntactic structure while listening? (2023.findings-acl)
Copied to clipboard
| Challenge: | Syntactic parsing is the task of assigning a syntactical structure to a sentence. |
| Approach: | They investigate the predictive power of the brain encoding models in three settings: individual performance of constituency and dependency parsing based embedding methods, relative effectiveness of each of the syntactic parsers, and relative importance of syntaktic information versus semantic information using BERT embeddngs. |
| Outcome: | The proposed methods explain variance in brain regions, especially in listening tasks, and the relative predictive power of the various syntax parsers is unexplored. |
A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces (2023.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms for the linguistically oriented exploration of large neural language models include treating the model as a linguistic test subject by measuring output on test sentences and building probing classifiers on top of embeddings to test whether the embeddables are sensitive to certain properties like dependency structure. |
| Approach: | They project contextual embeddings into interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. |
| Outcome: | The proposed method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes. |
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)
Copied to clipboard
| Challenge: | Semantic similarity modeling is central to many NLP problems such as question answering. |
| Approach: | They propose a pairwise word interaction model with syntactic structure priors to explore their effectiveness. |
| Outcome: | Extensive evaluations on eight benchmark datasets show that incorporating structural information improves over strong baselines. |
Paths to Relation Extraction through Semantic Structure (2021.findings-acl)
Copied to clipboard
| Challenge: | Syntactic and semantic structure directly reflect relations expressed by the text at hand and are therefore very useful for relation extraction (RE) |
| Approach: | They propose two methods for integrating broad-coverage semantic structure into supervised RE models by encoding semantic DAGs. |
| Outcome: | The proposed methods overshadow the use of syntactic integrations in RE . they reduce UCCA into a bilexical structure and encode semantic DAG structures . |
How Well Do Text Embedding Models Understand Syntax? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing text embedding models have not addressed syntactic understanding challenges, highlighting ineffectiveness and enhancing generalization ability. |
| Approach: | They propose to examine the ability of text embedding models to generalize across syntactic contexts. |
| Outcome: | The proposed models exhibit high similarity socres at this simple task. |
Retrofitting Structure-aware Transformer Language Model for End Tasks (2020.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. |
| Approach: | They propose to exploit syntactic distance to encode phrasal constituency and dependency connection into Transformer language model and leverage it for structure integration. |
| Outcome: | The proposed model achieves significant improvements for both semantic- and syntactic-dependent tasks. |
Learned Incremental Representations for Parsing (2022.acl-long)
Copied to clipboard
| Challenge: | a new syntactic representation that commits to syntakic choices is proposed for humans . we use a system that uses only incremental processing of a prefix to predict the word in a sentence . |
| Approach: | They propose a syntactic representation that commits to syntakic choices incrementally . they say the system can achieve 93.72 F1 on the Penn Treebank with as few as 5 bits per word . |
| Outcome: | The proposed representation achieves 93.72 F1 on the Penn Treebank with as few as 5 bits per word . the analysis of the representations shows they have interpretable features and deferred resolution of syntactic ambiguities. |