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Justin Vasselli

6 accepted papers

2025

Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?

Adam Nohejl, Frederikus Hudi, Eunike Andriani Kardinata, Shintaro Ozaki, Maria Angelica Riera Machin, Hongyu Sun +2
COLING 2025main

Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words even in the era of large language models (LLMs). Frequency in film subtitles has proved to be a particularly good approximation of everyday language exposure. For many languages, however, film sub…

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2025

CoAM: Corpus of All-Type Multiword Expressions

Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito +1
ACL 2025long

Multiword expressions (MWEs) refer to idiomatic sequences of multiple words.MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation, but existing datasets for the task are inconsistently annotated, limited to a single type of MWE, or limi…

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2025

Dictionaries to the Rescue: Cross-Lingual Vocabulary Transfer for Low-Resource Languages Using Bilingual Dictionaries

Haruki Sakajo, Yusuke Ide, Justin Vasselli, Yusuke Sakai, Yingtao Tian, Hidetaka Kamigaito +1
ACL 2025finding

Cross-lingual vocabulary transfer plays a promising role in adapting pre-trained language models to new languages, including low-resource languages.Existing approaches that utilize monolingual or parallel corpora face challenges when applied to languages with limited resources.In this work, we propo…

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2025

How to Make the Most of LLMs’ Grammatical Knowledge for Acceptability Judgments

Yusuke Ide, Yuto Nishida, Justin Vasselli, Miyu Oba, Yusuke Sakai, Hidetaka Kamigaito +1
NAACL 2025long

The grammatical knowledge of language models (LMs) is often measured using a benchmark of linguistic minimal pairs, where LMs are presented with a pair of acceptable and unacceptable sentences and required to judge which is more acceptable. Conventional approaches compare sentence probabilities dire…

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2025

Measuring the Robustness of Reference-Free Dialogue Evaluation Systems

Justin Vasselli, Adam Nohejl, Taro Watanabe
COLING 2025main

Advancements in dialogue systems powered by large language models (LLMs) have outpaced the development of reliable evaluation metrics, particularly for diverse and creative responses. We present a benchmark for evaluating the robustness of reference-free dialogue metrics against four categories of a…

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2025

Multilingual Dialogue Generation and Localization with Dialogue Act Scripting

Justin Vasselli, Eunike Andriani Kardinata, Yusuke Sakai, Taro Watanabe
EMNLP 2025

Non-English dialogue datasets are scarce, and models are often trained or evaluated on translations of English-language dialogues, an approach which can introduce artifacts that reduce their naturalness and cultural appropriateness. This work proposes Dialogue Act Script (DAS), a structured framewor

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