| Challenge: | a neural parser for transcribed speech can find EDITED disfluency nodes . this makes specialized mechanisms for parsing disfluencies unnecessary . |
| Approach: | They propose a neural self-attentive parser that finds EDITED disfluency nodes in transcribed speech. |
| Outcome: | The proposed parser finds EDITED disfluency nodes with an accuracy surpassing that of specialized systems. |
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What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
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| 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. |
Improving Disfluency Detection by Self-Training a Self-Attentive Model (2020.acl-main)
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| Challenge: | Existing self-attentive parsers using contextualized word embeddings produce state-of-the-art results in joint parsing and disfluency detection. |
| Approach: | They propose to use contextualized word embeddings to train a neural model using unlabeled data to train parsers. |
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Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information (N18-1)
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| Challenge: | acoustic signals provide cues that help listeners disambiguate difficult parses . speech carries useful extra information associated with prosodic structure . |
| Approach: | They propose a model that integrates transcribed text and acoustic-prosodic features into a neural network that accepts text and prosodic feature. |
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Constituency Parsing with a Self-Attentive Encoder (P18-1)
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| Challenge: | Recent work on LSTM encoders based on recurrent neural networks has led to improvements in constituency parsing accuracy. |
| Approach: | They propose to replace an LSTM encoder with a self-attentive architecture to improve a discriminative constituency parser. |
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Unsupervised Parsing via Constituency Tests (2020.emnlp-main)
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| Challenge: | Existing methods for unsupervised parsing rely on constituency tests . linguists can judge a sentence's grammatical validity by modifying it via some transformation . |
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Challenges to Open-Domain Constituency Parsing (2022.findings-acl)
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| Challenge: | Existing findings on cross-domain constituency parsing are only made on a limited number of domains. |
| Approach: | They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features. |
| Outcome: | The proposed model significantly improves the performance of the proposed model on the domain-variant features. |
Semantic Parsing of Disfluent Speech (2021.eacl-main)
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| Challenge: | Semantic parsing is a key component for understanding user utterances in voice assistants . however, most research on disfluent speech is focused on written text . |
| Approach: | They investigate semantic parsing of disfluent speech with the ATIS dataset . they add real and synthetic disfluencies at training time to improve model performance . |
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Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)
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| Challenge: | Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. |
| Approach: | They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances. |
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The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
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| 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 . |
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Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |