1 code implementation • 30 Apr 2024 • Shisen Yue, Siyuan Song, Xinyuan Cheng, Hai Hu
While all models generate largely fluent and self-consistent text, their explanations score low on reasonability except for GPT-4, suggesting that most LLMs cannot produce satisfactory explanations of the implicatures in the conversation.
1 code implementation • 11 Jan 2024 • Jushi Kai, Hai Hu, Zhouhan Lin
Therefore, we propose to ''highlight'' the factual information by selecting the tokens with the lowest probabilities and concatenating them to the original context, thus forcing the model to repeatedly read and hesitate on these tokens before generation.
1 code implementation • 15 Nov 2023 • Ziyin Zhang, Yikang Liu, Weifang Huang, Junyu Mao, Rui Wang, Hai Hu
In this work, we present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability -- MELA, with 46K samples covering 10 languages from a diverse set of language families.
1 code implementation • 23 May 2023 • Hai Hu, Ziyin Zhang, Weifang Huang, Jackie Yan-Ki Lai, Aini Li, Yina Patterson, Jiahui Huang, Peng Zhang, Chien-Jer Charles Lin, Rui Wang
We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language.
2 code implementations • 16 Apr 2023 • Yikang Liu, Ziyin Zhang, Wanyang Zhang, Shisen Yue, Xiaojing Zhao, Xinyuan Cheng, Yiwen Zhang, Hai Hu
To address these challenges in English language teaching, we first present ArguGPT, a balanced corpus of 4, 038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks.
1 code implementation • Findings (ACL) 2021 • Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Ma, Yanting Li, Yixin Nie, Kyle Richardson
These results, however, come with important caveats: cross-lingual models often perform best when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically translated resources (XNLI-zh).
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kuebler, Lawrence S. Moss
In this paper, we present the first large-scale NLI dataset (consisting of ~56, 000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI).
3 code implementations • COLING 2020 • Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
1 code implementation • SCiL 2020 • Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss, Sandra Kuebler
We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus.
3 code implementations • 16 Sep 2019 • Kyle Richardson, Hai Hu, Lawrence S. Moss, Ashish Sabharwal
Our experiments, using a library of 8 such semantic fragments, reveal two remarkable findings: (a) State-of-the-art models, including BERT, that are pre-trained on existing NLI benchmark datasets perform poorly on these new fragments, even though the phenomena probed here are central to the NLI task.
no code implementations • WS 2019 • Hai Hu, Wen Li, He Zhou, Zuoyu Tian, Yiwen Zhang, Liang Zou
This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese.
no code implementations • WS 2019 • Hai Hu, Qi Chen, Larry Moss
This paper describes a working system which performs natural language inference using polarity-marked parse trees.
1 code implementation • SEMEVAL 2018 • Hai Hu, Larry Moss
This paper shows how to take parse trees in CCG and algorithmically find the polarities of all the constituents.
no code implementations • WS 2018 • Hai Hu, Wen Li, Sandra Kübler
We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features.
no code implementations • 11 Mar 2018 • Hai Hu, Yiwen Zhang
He and Rao (2013) reported a raising phenomenon of /a/ in /Xan/ (X being a consonant or a vowel) in Chengdu dialect of Mandarin, i. e. /a/ is realized as [epsilon] for young speakers but [ae] for older speakers, but they offered no acoustic analysis.
no code implementations • 18 Nov 2017 • Hai Hu
This is one of the first studies that quantitatively examine the usage of English acronyms (e. g. WTO) in Chinese texts.
no code implementations • RANLP 2017 • Hai Hu, Daniel Dakota, S K{\"u}bler, ra
Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses.