Hai Zhao
17 papers in the PaperMetrix corpus
Papers by this author
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Converting Continuous-Space Language Models into <i>N</i> -gram Language Models with Efficient Bilingual Pruning for Statistical Machine Translation
2016 · ACM Transactions on Asian and Low-Resource Language Information Processing
The Language Model (LM) is an essential component of Statistical Machine Translation (SMT). In this article, we focus on developing efficient methods for LM construction. Our main contribution is that we propose a Natural N …
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English to Chinese Translation: How Chinese Character Matters
2015
Word segmentation is helpful in Chinese nat-ural language processing in many aspects. However it is showed that different word seg-mentation strategies do not affect the per-formance of Statistical Machine Translation (SMT) from English to Chinese …
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Subword ELMo
2019 · arXiv (Cornell University)
Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word representation to train language models.However, the character is an …
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Dual Multi-head Co-attention for Multi-choice Reading Comprehension.
2020 · arXiv (Cornell University)
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful pre-trained Language Model as …
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Dialogue Graph Modeling for Conversational Machine Reading
2021
Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks …
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What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension
2021
Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage. For extractive MRC, the answer span has been shown mostly determined …
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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
2024
Junlong Li, Jinyuan Wang, Zhuosheng Zhang, Hai Zhao. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
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Are LLMs Aware that Some Questions are not Open-ended?
2024 · arXiv (Cornell University)
Large Language Models (LLMs) have shown the impressive capability of answering questions in a wide range of scenarios. However, when LLMs face different types of questions, it is worth exploring whether LLMs are aware that …
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DocBench: A Benchmark for Evaluating LLM-based Document Reading Systems
2025
Anni Zou, Wenhao Yu, Hongming Zhang, Kaixin Ma, Deng Cai, Zhuosheng Zhang, Hai Zhao, Dong Yu. Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing. 2025.
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Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models
2025 · arXiv (Cornell University)
Word segmentation stands as a cornerstone of Natural Language Processing (NLP). Based on the concept of "comprehend first, segment later", we propose a new framework to explore the limit of unsupervised word segmentation with Large …
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How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition
2025 · arXiv (Cornell University)
How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival …
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Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
2015 · arXiv (Cornell University)
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten documents. While word embedding has been demoed as a powerful representation …
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A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding
2015 · arXiv (Cornell University)
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or handwritten documents. In this study, we propose to use BLSTM-RNN …
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Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
2019
Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations …
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Semantics-Aware BERT for Language Understanding
2020 · Proceedings of the AAAI Conference on Artificial Intelligence
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language …
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SG-Net: Syntax-Guided Machine Reading Comprehension
2020 · Proceedings of the AAAI Conference on Artificial Intelligence
For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to …
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Retrospective Reader for Machine Reading Comprehension
2021 · Proceedings of the AAAI Conference on Artificial Intelligence
Machine reading comprehension (MRC) is an AI challenge that requires machines to determine the correct answers to questions based on a given passage. MRC systems must not only answer questions when necessary but also tactfully …