Christopher Davis


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Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
Zheng Yuan | Shiva Taslimipoor | Christopher Davis | Christopher Bryant
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different error type tagsets derived from the ERRANT framework. Output from this detection system is used as auxiliary input to fine-tune a novel encoder-decoder GEC model, and we subsequently re-rank the N-best GEC output to find the hypothesis that most agrees with the GED output. Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall. This suggests that different multi-class GED systems benefit GEC in different ways. Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.


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Deconstructing multimodality: visual properties and visual context in human semantic processing
Christopher Davis | Luana Bulat | Anita Lilla Vero | Ekaterina Shutova
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks. Recent research has successfully used neural methods to automatically create visual representations for words. However, these works have extracted visual features from complete images, and have not examined how different kinds of visual information impact performance. In contrast, we construct multimodal models that differentiate between internal visual properties of the objects and their external visual context. We evaluate the models on the task of decoding brain activity associated with the meanings of nouns, demonstrating their advantage over those based on complete images.


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Decision Theory and Discourse Particles: A Case Study from a Large Japanese Sentiment Corpus
Christopher Davis
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation