Papers by Nikita Kitaev
Multilingual Constituency Parsing with Self-Attention and Pre-Training (P19-1)
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| Challenge: | a range of pre-training conditions can be used for constituency parsing, but large model sizes make it expensive to train separate models for each language. |
| Approach: | They compare the benefits of no pre-training, fastText, ELMo, and BERT for English . they also find that pre- training is beneficial across all 11 languages tested . |
| Outcome: | The proposed model outperforms fastText, ELMo, and BERT for English . but large model sizes make it expensive to train separate models for each language . |
Learned Incremental Representations for Parsing (2022.acl-long)
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| 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. |
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference (2020.acl-main)
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| Challenge: | Using custom architectures, constituency parsers are limited and require specialized hardware. |
| Approach: | They propose an algorithm that assigns labels to each word in a sentence in parallel and then performs a reconciliation phase to extract a tree in (empirically) linear time. |
| Outcome: | The proposed model achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies. |
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication (P19-1)
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Jin-Hwa Kim, Nikita Kitaev, Xinlei Chen, Marcus Rohrbach, Byoung-Tak Zhang, Yuandong Tian, Dhruv Batra, Devi Parikh
| Challenge: | a goal-driven collaborative drawing task combines language, perception, and actions in a partially observable environment . et al., 1990: 138K messages exchanged between human players. |
| Approach: | They propose a goal-driven collaborative task that combines language, perception, and action . they collect a clip art dataset and use it to build an image-drawing game between two agents . |
| Outcome: | The proposed task integrates language, perception, and action in a virtual world . it is based on a dataset of 10K dialogs and 138K messages exchanged between humans . |
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. |
| Outcome: | The proposed model outperforms the previous best-published results on 8 of the 9 languages in the SPMRL dataset. |
Cross-Domain Generalization of Neural Constituency Parsers (P19-1)
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| Challenge: | Neural parsers perform well on in-domain benchmarks, but their performance degrades in well-understood ways. |
| Approach: | They analyze generalization on English and Chinese corpora to see if they can generalize to other domains. |
| Outcome: | The proposed neural parsers perform better on in-domain benchmarks than on out-of-domain corpora. |
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 . |
| Approach: | They propose a method for unsupervised parsing based on a constituency test . they specify a set of transformations and use an unsupervised neural acceptability model to make grammaticality decisions. |
| Outcome: | The proposed method achieves 62.8 F1 on the Penn Treebank test set, an improvement of 7.6 points over the previous best results. |