Papers by Hiroki Ouchi
You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations (2020.aacl-main)
Copied to clipboard
| Challenge: | Several studies have addressed explainable recommendations that produce natural language sentences . however, this task cannot explain detailed evidences for each hotel . |
| Approach: | They propose to decompose the process into two subtasks: Evidence Identification and Evidence Explanation. |
| Outcome: | The proposed model can explain evidences in recommending hotels given vague requests . it can find evidence sentences with respect to various vague requests and generate recommendation sentences . |
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring (P19-2)
Copied to clipboard
| Challenge: | Existing document embedding approaches focus on capturing sequences of words in documents . however, some document classification and regression tasks need to consider discourse structure of text . |
| Approach: | They propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require expensive parsers or annotation. |
| Outcome: | The proposed method improves essay Organization scoring and Argument Strength scoring. |
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis (D19-1)
Copied to clipboard
| Challenge: | despite its practical advantages, transductive learning is underexplored in natural language processing . despite the simplicity of the technique, it is understudied in natural languages . |
| Approach: | They conduct an empirical study of transductive learning for neural models . they fine-tune language models on an unlabeled test set to obtain test-set-specific word representations. |
| Outcome: | The proposed method improves state-of-the-art neural models in syntactic and semantic tasks. |
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation (2024.findings-eacl)
Copied to clipboard
Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
| Challenge: | et al., 2006) considers geographic relatedness among geo-entity mentions in document-level geoparsing. |
| Approach: | They present a Japanese travelogue dataset that considers geographic relatedness among geo-entity mentions. |
| Outcome: | The proposed dataset includes 200 travelogue documents with rich geo-entity information . it shows that human activities, mobility, and events are often described with natural language expressions of locations or geographic entities (geo-entities) |
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution (2021.emnlp-main)
Copied to clipboard
| Challenge: | Masked language models have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). |
| Approach: | They propose a pretraining task that trains MLMs on anaphoric relations with explicit supervision and a finetuning method that remedies a notorious discrepancy. |
| Outcome: | The proposed method improves zero anaphora resolution in Japanese ZAR . it uses a pretrain task and finetuning task to correct the discrepancy . |
AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models outperform them in certain domains, indicating that there is significant potential for further improvement in this area. |
| Approach: | They propose to use pre-trained language models to evaluate ad texts from multiple perspectives within real-world advertising operations to define five tasks and construct a Japanese dataset. |
| Outcome: | The proposed benchmark outperforms existing pre-trained language models in several tasks, but humans outperformed them in certain domains. |
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition (2020.acl-main)
Copied to clipboard
Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
| Challenge: | Recent neural networks can induce good span feature representations and achieve high performance in structured prediction tasks. |
| Approach: | They propose an instance-based learning method that learns similarities between spans . they aim to build models that have high interpretability without sacrificing performance . |
| Outcome: | The proposed method improves interpretability without sacrificing performance. |
An Empirical Study of Span Representations in Argumentation Structure Parsing (P19-1)
Copied to clipboard
Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui
| Challenge: | Argumentation structure parsing (ASP) is a task of identifying argumentation structures in argumentative text. |
| Approach: | They propose to exploit neural network-based span representations for ASP to improve performance . they also propose task-dependent extensions for a parser that can be used to parse arguments . |
| Outcome: | The proposed model outperforms neural network-based approaches for argumentation structure parsing (ASP) it also provides some challenging types of instances to be parsed. |
A Text Embedding Model with Contrastive Example Mining for Point-of-Interest Geocoding (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies have focused on coarse-grained locations, but we focus on fine-grain POIs, which have many candidates with similar names. |
| Approach: | They develop a text embedding-based geocoding model and investigate (1) entry encoding representations and (2) hard negative mining approaches suitable for enhancing the model’s disambiguation ability. |
| Outcome: | The proposed model significantly improves its disambiguation ability and entry encoding representations. |
A Span Selection Model for Semantic Role Labeling (D18-1)
Copied to clipboard
| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |
Evaluating Dialogue Generation Systems via Response Selection (2020.acl-main)
Copied to clipboard
| Challenge: | Existing automatic evaluation metrics for open-domain dialogue systems correlate poorly with human evaluation. |
| Approach: | They propose to construct response selection test sets with well-chosen false candidates to evaluate response generation systems via response selection. |
| Outcome: | The proposed method correlates with human evaluation better than widely used metrics such as BLEU. |
Modeling Overregularization in Children with Small Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing research has analyzed regularization in language acquisition only by modeling word inflection directly, which is unnatural in light of human language acquisition. |
| Approach: | They hypothesize that language models that imitate errors children make during language acquisition have a learning process more similar to humans. |
| Outcome: | The proposed model shows child-like U-shaped learning curves clearly for certain verbs, but the preferences for types of overgeneralization did not fully match the observations in children. |
Constructing Indonesian-English Travelogue Dataset (2024.lrec-main)
Copied to clipboard
| Challenge: | low-resource language research often hampered due to under-representation of how it is being used in reality. |
| Approach: | They propose to use a dataset comprising both Indonesian and English from personal travelogue articles . they used named and nominal expressions of four entity types related to travel . |
| Outcome: | The proposed dataset is more representative of how Indonesian language is being used in reality. |
Automatic Generation of a Compositional QA Benchmark for Geospatial Reasoning under Spatial and Entity Constraints (2026.eacl-srw)
Copied to clipboard
| Challenge: | Recent advances in large language models have enhanced their ability to perform reasoning tasks that integrate linguistic, visual, and factual information. |
| Approach: | They propose a method for constructing compositional geographic question answering datasets that jointly consider spatial and entity constraints. |
| Outcome: | The proposed method performs well on questions involving rich entity grounding, but its accuracy drops on quantitative spatial reasoning questions. |
Second Language Acquisition of Neural Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | a recent study examined the cross-lingual transferability of neural language models . previous studies focused on their first language acquisition . |
| Approach: | They propose to pretrain bilingual LMs with a scenario similar to human L2 acquisition . they find that pretraining accelerated their linguistic generalization in L2 . |
| Outcome: | The results show that pretraining bilingual LMs accelerates their linguistic generalizations . the results clarify their (non-)human-like L2 acquisition in particular aspects . |
Graph-Structured Trajectory Extraction from Travelogues (2025.acl-long)
Copied to clipboard
Aitaro Yamamoto, Hiroyuki Otomo, Hiroki Ouchi, Shohei Higashiyama, Hiroki Teranishi, Hiroyuki Shindo, Taro Watanabe
| Challenge: | Existing studies treat travelogues as sequences of visited locations, but they lack a benchmark dataset. |
| Approach: | They propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order and construct a benchmark dataset for the extraction. |
| Outcome: | The proposed dataset shows that even naive baseline systems can predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations. |
Instance-Based Neural Dependency Parsing (2021.tacl-1)
Copied to clipboard
Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Masashi Yoshikawa, Kentaro Inui
| Challenge: | Existing models that use instance-based inference for dependency parsing are difficult to understand for humans. |
| Approach: | They develop neural models that adopt an interpretable inference process for dependency parsing. |
| Outcome: | The proposed models achieve competitive accuracy with standard neural models and have plausibility of instance-based explanations. |
Addressee and Response Selection for Multilingual Conversation (C18-1)
Copied to clipboard
| Challenge: | Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. |
| Approach: | They propose multilingual addressee and response selection task for conversational systems . they use a multilingual conversation dataset to evaluate their methods . |
| Outcome: | The proposed methods can predict addressee and response in multiple languages . they show that the methods work in a multilingual conversation dataset . |
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to augment labeled data are limited by the scarcity of labeles . a method called contextual data augmentation (CDA) can be used to augment labels . |
| Approach: | They propose a data augmentation method that generates labeled training instances using a pretrained language model. |
| Outcome: | The proposed method can improve the quality of augmented training data compared to the conventional method. |
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition (2020.acl-srw)
Copied to clipboard
| 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. |
BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Web banner advertisements are often selected manually because of human preferences . a new benchmark evaluates the degree of alignment with human preferences in two tasks . |
| Approach: | a benchmark was developed to evaluate the human preference-driven banner selection process using vision-language models. |
| Outcome: | The proposed benchmark assesses the degree of alignment with human preferences in two tasks using vision-language models. |
Can Language Models Induce Grammatical Knowledge from Indirect Evidence? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in language models have shown remarkable progress in various tasks. |
| Approach: | They introduce a dataset that incorporates wug words and inject them into pretraining data and evaluate them on evaluation data. |
| Outcome: | The proposed model does not induce grammatical knowledge even after repeated exposure to instances with the same structure but differing only in lexical items from evaluation instances in certain language phenomena. |
JaCorpTrack: Corporate History Event Extraction for Tracking Organizational Changes (2025.emnlp-industry)
Copied to clipboard
Yuya Sawada, Hiroki Ouchi, Yuichiro Yasui, Hiroki Teranishi, Yuji Matsumoto, Taro Watanabe, Masayuki Ishii
| Challenge: | Existing information extraction systems are not able to accurately capture organizational changes. |
| Approach: | They propose a task to extract corporate history events related to organizational changes by identifying company names before and after each event, as well as the corresponding date. |
| Outcome: | The proposed task is designed to identify company names before and after an event, as well as the corresponding date. |
Inject Rubrics into Short Answer Grading System (D19-61)
Copied to clipboard
| Challenge: | Short Answer Grading (SAG) is a task of scoring students’ answers in examinations. Existing SAG systems only predict scores based on the answers, but they ignore important evaluation criteria such as rubrics. |
| Approach: | They propose to inject rubrics into SAG models by introducing word-level attention mechanism into the model to locate information in each answer that are highly related to the score. |
| Outcome: | The proposed model outperforms the state-of-the-art model on the widely used ASAP-SAS dataset under low-resource settings. |
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling (2022.coling-1)
Copied to clipboard
| Challenge: | Semantic role labeling is the task of labeling semantic arguments for marked semantic predicates. |
| Approach: | They propose a model which combines global decoding and iterative identification for the semantic arguments to consider their roles and relations in the labeling order. |
| Outcome: | The proposed model outperforms existing models in the benchmark datasets of span-based SRL: CoNLL-2005 and CoNll-2012. |