Papers by Ryoko Tokuhisa
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. |
Target-Guided Open-Domain Conversation Planning (2022.coling-1)
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| Challenge: | Existing studies on goal-oriented conversational tasks lack planning . prior studies on this topic have focused on generating proactive behavior in agents . |
| Approach: | They propose a task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. |
| Outcome: | The proposed task evaluates whether neural conversational agents have goal-oriented conversation planning abilities. |
Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems (2022.aacl-srw)
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| Challenge: | Existing methods for dialogue system evaluation are inefficient and time-consuming. |
| Approach: | They propose a dialogue collection method for automating dialogue system evaluation using bipartite-play method . authors propose constructing a better automatic evaluation method which is reproducible and low cost . |
| Outcome: | The proposed method correlates strongly with human subjectivity and human evaluation. |
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing methods for pre-trained language models lack explicit grounding in real-world entities. |
| Approach: | They propose a mechanism that integrates the structure of a KG into recent PLM architectures by generalizing the embeddings of neighboring entities. |
| Outcome: | The proposed method improves a classification task, entity typing task and language comprehension tasks. |