Papers by Kaori Abe
MQM-Chat: Multidimensional Quality Metrics for Chat Translation (2025.coling-main)
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| Challenge: | Existing methods for chat translation face challenges due to high levels of ambiguity and stylized contents. |
| Approach: | They propose a multidimensional quality metric for chat translation that includes seven error types . they use human annotations to analyze chat data generated by five translation models . |
| Outcome: | The proposed evaluation metric can qualify errors while highlighting chat-specific issues explicitly. |
Topicalization in Language Models: A Case Study on Japanese (2022.coling-1)
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| Challenge: | a recent study has shown that neural language models can capture discourse-level preferences in text generation . a particular aspect of discourse is the topic-comment structure . |
| Approach: | They analyze whether neural language models can capture discourse-level preferences in text generation . they use Japanese language and crowdsourced human topicalization judgment data . |
| Outcome: | The proposed model can capture human-like generalizations in discourse-level linguistic aspects. |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition (2020.acl-srw)
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| Challenge: | In general, the labels used in sequence labeling consist of different types of elements. |
| Approach: | They propose to integrate label component information as embeddings into sequence labeling models. |
| Outcome: | The proposed method improves on English and Japanese fine-grained named entity recognition on low-frequency labels. |