Haizhou Li


2023

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Dynamic Transformers Provide a False Sense of Efficiency
Yiming Chen | Simin Chen | Zexin Li | Wei Yang | Cong Liu | Robby Tan | Haizhou Li
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference. Multi-exit is a mainstream approach to address this issue by making a trade-off between efficiency and accuracy, where the saving of computation comes from an early exit. However, whether such saving from early-exiting is robust remains unknown. Motivated by this, we first show that directly adapting existing adversarial attack approaches targeting model accuracy cannot significantly reduce inference efficiency. To this end, we propose a simple yet effective attacking framework, SAME, a novel slowdown attack framework on multi-exit models, which is specially tailored to reduce the efficiency of the multi-exit models. By leveraging the multi-exit models’ design characteristics, we utilize all internal predictions to guide the adversarial sample generation instead of merely considering the final prediction. Experiments on the GLUE benchmark show that SAME can effectively diminish the efficiency gain of various multi-exit models by 80% on average, convincingly validating its effectiveness and generalization ability.

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xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark
Chen Zhang | Luis D’Haro | Chengguang Tang | Ke Shi | Guohua Tang | Haizhou Li
Findings of the Association for Computational Linguistics: EMNLP 2023

Recent advancements in reference-free learned metrics for open-domain dialogue evaluation have been driven by the progress in pre-trained language models and the availability of dialogue data with high-quality human annotations. However, current studies predominantly concentrate on English dialogues, and the generalization of these metrics to other languages has not been fully examined. This is largely due to the absence of a multilingual dialogue evaluation benchmark. To address the issue, we introduce xDial-Eval, built on top of open-source English dialogue evaluation datasets. xDial-Eval includes 12 turn-level and 6 dialogue-level English datasets, comprising 14930 annotated turns and 8691 annotated dialogues respectively. The English dialogue data are extended to nine other languages with commercial machine translation systems. On xDial-Eval, we conduct comprehensive analyses of previous BERT-based metrics and the recently-emerged large language models. Lastly, we establish strong self-supervised and multilingual baselines. In terms of average Pearson correlations over all datasets and languages, the best baseline outperforms OpenAI’s ChatGPT by absolute improvements of 6.5% and 4.6% at the turn and dialogue levels respectively, albeit with much fewer parameters. The data and code are publicly available at https://github.com/e0397123/xDial-Eval.

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How Well Do Text Embedding Models Understand Syntax?
Yan Zhang | Zhaopeng Feng | Zhiyang Teng | Zuozhu Liu | Haizhou Li
Findings of the Association for Computational Linguistics: EMNLP 2023

Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability of these models to generalize across a wide range of syntactic contexts remains under-explored. In this paper, we first develop an evaluation set, named SR, to scrutinize the capability for syntax understanding of text embedding models from two crucial syntactic aspects: Structural heuristics, and Relational understanding among concepts, as revealed by the performance gaps in previous studies. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges, and such ineffectiveness becomes even more apparent when evaluated against existing benchmark datasets. Furthermore, we conduct rigorous analysis to unearth factors that lead to such limitations and examine why previous evaluations fail to detect such ineffectiveness. Lastly, we propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios. This study serves to highlight the hurdles associated with syntactic generalization and provides pragmatic guidance for boosting model performance across varied syntactic contexts.

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HuatuoGPT, Towards Taming Language Model to Be a Doctor
Hongbo Zhang | Junying Chen | Feng Jiang | Fei Yu | Zhihong Chen | Guiming Chen | Jianquan Li | Xiangbo Wu | Zhang Zhiyi | Qingying Xiao | Xiang Wan | Benyou Wang | Haizhou Li
Findings of the Association for Computational Linguistics: EMNLP 2023

In this paper, we present HuatuoGPT, a Large Language Model (LLM) for medical consultation. The core recipe of HuatuoGPT is to leverage both distilled data from **ChatGPT** and real-world data from **doctors** in the supervised fine-tuning stage. This is not only because purely using **ChatGPT**-distilled data might cause ‘model collapse’, but also because real-world data from **doctors** would be complementary to **ChatGPT**-distilled data. The responses from ChatGPT are usually detailed, well-presented, fluent, and instruction-followed, but it cannot perform like a doctor in many aspects, e.g. for interactive diagnosis. Therefore, the extra doctors’ data could tame a distilled language model to perform like doctors. To synergize the strengths of both data sources, we introduce RLMF (Reinforcement Learning from Mixed Feedback) where a reward model is trained to align the language model with the merits that both sources (ChatGPT and doctors) bring. Experimental results (in GPT-4 evaluation, human evaluation, and medical benchmark datasets) demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs. It is worth noting that by using additional real-world data and RLMF, the distilled language model (i.e., HuatuoGPT) outperforms its teacher model (i.e., ChatGPT) in most cases.

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Relational Sentence Embedding for Flexible Semantic Matching
Bin Wang | Haizhou Li
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

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Speech-Aware Multi-Domain Dialogue State Generation with ASR Error Correction Modules
Ridong Jiang | Wei Shi | Bin Wang | Chen Zhang | Yan Zhang | Chunlei Pan | Jung Jae Kim | Haizhou Li
Proceedings of The Eleventh Dialog System Technology Challenge

Prior research on dialogue state tracking (DST) is mostly based on written dialogue corpora. For spoken dialogues, the DST model trained on the written text should use the results (or hypothesis) of automatic speech recognition (ASR) as input. But ASR hypothesis often includes errors, which leads to significant performance drop for spoken dialogue state tracking. We address the issue by developing the following ASR error correction modules. First, we train a model to convert ASR hypothesis to ground truth user utterance, which can fix frequent patterns of errors. The model takes ASR hypotheses of two ASR models as input and fine-tuned in two stages. The corrected hypothesis is fed into a large scale pre-trained encoder-decoder model (T5) for DST training and inference. Second, if an output slot value from the encoder-decoder model is a name, we compare it with names in a dictionary crawled from Web sites and, if feasible, replace with the crawled name of the shortest edit distance. Third, we fix errors of temporal expressions in ASR hypothesis by using hand-crafted rules. Experiment results on the DSTC 11 speech-aware dataset, which is built on the popular MultiWOZ task (version 2.1), show that our proposed method can effectively mitigate the performance drop when moving from written text to spoken conversations.

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Text-Derived Language Identity Incorporation for End-to-End Code-Switching Speech Recognition
Qinyi Wang | Haizhou Li
Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching

Recognizing code-switching (CS) speech often presents challenges for an automatic speech recognition system (ASR) due to limited linguistic context in short monolingual segments, resulting in language confusion. To mitigate this issue, language identity (LID) is often integrated into the speech recognition system to provide additional linguistic context. However, previous works predominately focus on extracting language identity from speech signals. We introduce a novel approach to learn language identity from pure text data via a dedicated language identity-language model. Besides, we explore two strategies: LID state fusion and language posterior biasing, to integrate the text-derived language identities into the end-to-end ASR system. By incorporating hypothesized language identities, our ASR system gains crucial contextual cues, effectively capturing language transitions and patterns within code-switched utterances. We conduct speech recognition experiments on the SEAME corpus and demonstrate the effectiveness of our proposed methods. Our results reveal significantly improved transcriptions in code-switching scenarios, underscoring the potential of text-derived LID in enhancing code-switching speech recognition.

2022

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M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database
Jinming Zhao | Tenggan Zhang | Jingwen Hu | Yuchen Liu | Qin Jin | Xinchao Wang | Haizhou Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The emotional state of a speaker can be influenced by many different factors in dialogues, such as dialogue scene, dialogue topic, and interlocutor stimulus. The currently available data resources to support such multimodal affective analysis in dialogues are however limited in scale and diversity. In this work, we propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. M3ED is annotated with 7 emotion categories (happy, surprise, sad, disgust, anger, fear, and neutral) at utterance level, and encompasses acoustic, visual, and textual modalities. To the best of our knowledge, M3ED is the first multimodal emotional dialogue dataset in Chinese.It is valuable for cross-culture emotion analysis and recognition. We apply several state-of-the-art methods on the M3ED dataset to verify the validity and quality of the dataset. We also propose a general Multimodal Dialogue-aware Interaction framework, MDI, to model the dialogue context for emotion recognition, which achieves comparable performance to the state-of-the-art methods on the M3ED. The full dataset and codes are available.

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Just Rank: Rethinking Evaluation with Word and Sentence Similarities
Bin Wang | C.-C. Jay Kuo | Haizhou Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method. It leads models to overfit to such evaluations, negatively impacting embedding models’ development. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. Further, we propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks. Extensive experiments are conducted based on 60+ models and popular datasets to certify our judgments. Finally, the practical evaluation toolkit is released for future benchmarking purposes.

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FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation
Chen Zhang | Luis Fernando D’Haro | Qiquan Zhang | Thomas Friedrichs | Haizhou Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment. However, they either perform turn-level evaluation or look at a single dialogue quality dimension. One would expect a good evaluation metric to assess multiple quality dimensions at the dialogue level. To this end, we are motivated to propose a multi-dimensional dialogue-level metric, which consists of three sub-metrics with each targeting a specific dimension. The sub-metrics are trained with novel self-supervised objectives and exhibit strong correlations with human judgment for their respective dimensions. Moreover, we explore two approaches to combine the sub-metrics: metric ensemble and multitask learning. Both approaches yield a holistic metric that significantly outperforms individual sub-metrics. Compared to the existing state-of-the-art metric, the combined metrics achieve around 16% relative improvement on average across three high-quality dialogue-level evaluation benchmarks.

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Analyzing and Evaluating Faithfulness in Dialogue Summarization
Bin Wang | Chen Zhang | Yan Zhang | Yiming Chen | Haizhou Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Dialogue summarization is abstractive in nature, making it suffer from factual errors. The factual correctness of summaries has the highest priority before practical applications. Many efforts have been made to improve faithfulness in text summarization. However, there is a lack of systematic study on dialogue summarization systems. In this work, we first perform the fine-grained human analysis on the faithfulness of dialogue summaries and observe that over 35% of generated summaries are faithfully inconsistent respective the source dialogues. Furthermore, we present a new model-level faithfulness evaluation method. It examines generation models with multi-choice questions created by rule-based transformations. Experimental results show that our evaluation schema is a strong proxy for the factual correctness of summarization models. The human-annotated faithfulness samples and the evaluation toolkit are released to facilitate future research toward faithful dialogue summarization.

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Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Yiming Chen | Yan Zhang | Bin Wang | Zuozhu Liu | Haizhou Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performance of unsupervised methods typically lags far behind that of the supervised counterparts in most downstream tasks. In this work, we propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data. Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data. Comprehensive experiments show that GenSE achieves an average correlation score of 85.19 on the STS datasets and consistent performance improvement on four domain adaptation tasks, significantly surpassing the state-of-the-art methods and convincingly corroborating its effectiveness and generalization ability.

2021

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Bootstrapped Unsupervised Sentence Representation Learning
Yan Zhang | Ruidan He | Zuozhu Liu | Lidong Bing | Haizhou Li
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention. In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence. Specifically, given one augmented view of the input sentence, the online network branch is trained by predicting the representation yielded by the target network of the same sentence under another augmented view. Meanwhile, the target network branch is bootstrapped with a moving average of the online network. The proposed method significantly outperforms other state-of-the-art unsupervised methods on semantic textual similarity (STS) and classification tasks. It can be adopted as a post-training procedure to boost the performance of the supervised methods. We further extend our method for learning multilingual sentence representations and demonstrate its effectiveness on cross-lingual STS tasks. Our code is available at https://github.com/yanzhangnlp/BSL.

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DynaEval: Unifying Turn and Dialogue Level Evaluation
Chen Zhang | Yiming Chen | Luis Fernando D’Haro | Yan Zhang | Thomas Friedrichs | Grandee Lee | Haizhou Li
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.

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Revisiting Self-training for Few-shot Learning of Language Model
Yiming Chen | Yan Zhang | Chen Zhang | Grandee Lee | Ran Cheng | Haizhou Li
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM. Given two views of a text sample via weak and strong augmentation techniques, SFLM generates a pseudo label on the weakly augmented version. Then, the model predicts the same pseudo label when fine-tuned with the strongly augmented version. This simple approach is shown to outperform other state-of-the-art supervised and semi-supervised counterparts on six sentence classification and six sentence-pair classification benchmarking tasks. In addition, SFLM only relies on a few in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate the robustness of our proposed approach under various settings, including augmentation techniques, model scale, and few-shot knowledge transfer across tasks.

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Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Haizhou Li | Gina-Anne Levow | Zhou Yu | Chitralekha Gupta | Berrak Sisman | Siqi Cai | David Vandyke | Nina Dethlefs | Yan Wu | Junyi Jessy Li
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

2020

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Modeling Code-Switch Languages Using Bilingual Parallel Corpus
Grandee Lee | Haizhou Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Language modeling is the technique to estimate the probability of a sequence of words. A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages. We propose a bilingual attention language model (BALM) that simultaneously performs language modeling objective with a quasi-translation objective to model both the monolingual as well as the cross-lingual sequential dependency. The attention mechanism learns the bilingual context from a parallel corpus. BALM achieves state-of-the-art performance on the SEAME code-switch database by reducing the perplexity of 20.5% over the best-reported result. We also apply BALM in bilingual lexicon induction, and language normalization tasks to validate the idea.

2018

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Proceedings of the Seventh Named Entities Workshop
Nancy Chen | Rafael E. Banchs | Xiangyu Duan | Min Zhang | Haizhou Li
Proceedings of the Seventh Named Entities Workshop

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Named-Entity Tagging and Domain adaptation for Better Customized Translation
Zhongwei Li | Xuancong Wang | Ai Ti Aw | Eng Siong Chng | Haizhou Li
Proceedings of the Seventh Named Entities Workshop

Customized translation need pay spe-cial attention to the target domain ter-minology especially the named-entities for the domain. Adding linguistic features to neural machine translation (NMT) has been shown to benefit translation in many studies. In this paper, we further demonstrate that adding named-entity (NE) feature with named-entity recognition (NER) into the source language produces better translation with NMT. Our experiments show that by just including the different NE classes and boundary tags, we can increase the BLEU score by around 1 to 2 points using the standard test sets from WMT2017. We also show that adding NE tags using NER and applying in-domain adaptation can be combined to further improve customized machine translation.

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NEWS 2018 Whitepaper
Nancy Chen | Xiangyu Duan | Min Zhang | Rafael E. Banchs | Haizhou Li
Proceedings of the Seventh Named Entities Workshop

Transliteration is defined as phonetic translation of names across languages. Transliteration of Named Entities (NEs) is necessary in many applications, such as machine translation, corpus alignment, cross-language IR, information extraction and automatic lexicon acquisition. All such systems call for high-performance transliteration, which is the focus of shared task in the NEWS 2018 workshop. The objective of the shared task is to promote machine transliteration research by providing a common benchmarking platform for the community to evaluate the state-of-the-art technologies.

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Report of NEWS 2018 Named Entity Transliteration Shared Task
Nancy Chen | Rafael E. Banchs | Min Zhang | Xiangyu Duan | Haizhou Li
Proceedings of the Seventh Named Entities Workshop

This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia. Similar to previous editions of NEWS, the Shared Task featured 19 tasks on proper name transliteration, including 13 different languages and two different Japanese scripts. A total of 6 teams from 8 different institutions participated in the evaluation, submitting 424 runs, involving different transliteration methodologies. Four performance metrics were used to report the evaluation results. The NEWS shared task on machine transliteration has successfully achieved its objectives by providing a common ground for the research community to conduct comparative evaluations of state-of-the-art technologies that will benefit the future research and development in this area.

2016

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Proceedings of the Sixth Named Entity Workshop
Xiangyu Duan | Rafael E. Banchs | Min Zhang | Haizhou Li | A Kumaran
Proceedings of the Sixth Named Entity Workshop

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Evaluating and Combining Name Entity Recognition Systems
Ridong Jiang | Rafael E. Banchs | Haizhou Li
Proceedings of the Sixth Named Entity Workshop

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Whitepaper of NEWS 2016 Shared Task on Machine Transliteration
Xiangyu Duan | Min Zhang | Haizhou Li | Rafael Banchs | A Kumaran
Proceedings of the Sixth Named Entity Workshop

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Report of NEWS 2016 Machine Transliteration Shared Task
Xiangyu Duan | Rafael Banchs | Min Zhang | Haizhou Li | A. Kumaran
Proceedings of the Sixth Named Entity Workshop

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Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking
Seokhwan Kim | Rafael Banchs | Haizhou Li
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Proceedings of the Fifth Named Entity Workshop
Xiangyu Duan | Rafael E. Banchs | Min Zhang | Haizhou Li | A Kumaran
Proceedings of the Fifth Named Entity Workshop

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Whitepaper of NEWS 2015 Shared Task on Machine Transliteration
Min Zhang | Haizhou Li | Rafael E. Banchs | A Kumaran
Proceedings of the Fifth Named Entity Workshop

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Report of NEWS 2015 Machine Transliteration Shared Task
Rafael E. Banchs | Min Zhang | Xiangyu Duan | Haizhou Li | A. Kumaran
Proceedings of the Fifth Named Entity Workshop

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Towards Improving Dialogue Topic Tracking Performances with Wikification of Concept Mentions
Seokhwan Kim | Rafael E. Banchs | Haizhou Li
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Wikification of Concept Mentions within Spoken Dialogues Using Domain Constraints from Wikipedia
Seokhwan Kim | Rafael E. Banchs | Haizhou Li
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia
Seokhwan Kim | Rafael E. Banchs | Haizhou Li
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Broadcast News Story Segmentation Using Manifold Learning on Latent Topic Distributions
Xiaoming Lu | Lei Xie | Cheung-Chi Leung | Bin Ma | Haizhou Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Modeling of term-distance and term-occurrence information for improving n-gram language model performance
Tze Yuang Chong | Rafael E. Banchs | Eng Siong Chng | Haizhou Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Meaning Unit Segmentation in English and Chinese: a New Approach to Discourse Phenomena
Jennifer Williams | Rafael Banchs | Haizhou Li
Proceedings of the Workshop on Discourse in Machine Translation

2012

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Proceedings of the 4th Named Entity Workshop (NEWS) 2012
Min Zhang | Haizhou Li | A Kumaran
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

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Whitepaper of NEWS 2012 Shared Task on Machine Transliteration
Min Zhang | Haizhou Li | A Kumaran | Ming Liu
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

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Report of NEWS 2012 Machine Transliteration Shared Task
Min Zhang | Haizhou Li | A Kumaran | Ming Liu
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

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Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Haizhou Li | Chin-Yew Lin | Miles Osborne | Gary Geunbae Lee | Jong C. Park
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Utilizing Dependency Language Models for Graph-based Dependency Parsing Models
Wenliang Chen | Min Zhang | Haizhou Li
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Modeling the Translation of Predicate-Argument Structure for SMT
Deyi Xiong | Min Zhang | Haizhou Li
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Haizhou Li | Chin-Yew Lin | Miles Osborne | Gary Geunbae Lee | Jong C. Park
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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IRIS: a Chat-oriented Dialogue System based on the Vector Space Model
Rafael E. Banchs | Haizhou Li
Proceedings of the ACL 2012 System Demonstrations

2011

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CLGVSM: Adapting Generalized Vector Space Model to Cross-lingual Document Clustering
Guoyu Tang | Yunqing Xia | Min Zhang | Haizhou Li | Fang Zheng
Proceedings of 5th International Joint Conference on Natural Language Processing

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Joint Alignment and Artificial Data Generation: An Empirical Study of Pivot-based Machine Transliteration
Min Zhang | Xiangyu Duan | Ming Liu | Yunqing Xia | Haizhou Li
Proceedings of 5th International Joint Conference on Natural Language Processing

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SMT Helps Bitext Dependency Parsing
Wenliang Chen | Jun’ichi Kazama | Min Zhang | Yoshimasa Tsuruoka | Yujie Zhang | Yiou Wang | Kentaro Torisawa | Haizhou Li
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Joint Models for Chinese POS Tagging and Dependency Parsing
Zhenghua Li | Min Zhang | Wanxiang Che | Ting Liu | Wenliang Chen | Haizhou Li
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 3rd Named Entities Workshop (NEWS 2011)
Min Zhang | Haizhou Li | A Kumaran
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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Report of NEWS 2011 Machine Transliteration Shared Task
Min Zhang | Haizhou Li | A Kumaran | Ming Liu
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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Whitepaper of NEWS 2011 Shared Task on Machine Transliteration
Min Zhang | A Kumaran | Haizhou Li
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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Enhancing Language Models in Statistical Machine Translation with Backward N-grams and Mutual Information Triggers
Deyi Xiong | Min Zhang | Haizhou Li
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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AM-FM: A Semantic Framework for Translation Quality Assessment
Rafael E. Banchs | Haizhou Li
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Learning Translation Boundaries for Phrase-Based Decoding
Deyi Xiong | Min Zhang | Haizhou Li
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Non-Isomorphic Forest Pair Translation
Hui Zhang | Min Zhang | Haizhou Li | Eng Siong Chng
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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EM-based Hybrid Model for Bilingual Terminology Extraction from Comparable Corpora
Lianhau Lee | Aiti Aw | Min Zhang | Haizhou Li
Coling 2010: Posters

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Improving Name Origin Recognition with Context Features and Unlabelled Data
Vladimir Pervouchine | Min Zhang | Ming Liu | Haizhou Li
Coling 2010: Posters

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Machine Transliteration: Leveraging on Third Languages
Min Zhang | Xiangyu Duan | Vladimir Pervouchine | Haizhou Li
Coling 2010: Posters

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Pseudo-Word for Phrase-Based Machine Translation
Xiangyu Duan | Min Zhang | Haizhou Li
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Error Detection for Statistical Machine Translation Using Linguistic Features
Deyi Xiong | Min Zhang | Haizhou Li
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Convolution Kernel over Packed Parse Forest
Min Zhang | Hui Zhang | Haizhou Li
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Linguistically Annotated Reordering: Evaluation and Analysis
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Computational Linguistics, Volume 36, Issue 3 - September 2010

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I2R’s machine translation system for IWSLT 2010
Xiangyu Duan | Rafael Banchs | Jun Lang | Deyi Xiong | Aiti Aw | Min Zhang | Haizhou Li
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

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Proceedings of the 2010 Named Entities Workshop
A Kumaran | Haizhou Li
Proceedings of the 2010 Named Entities Workshop

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Report of NEWS 2010 Transliteration Generation Shared Task
Haizhou Li | A Kumaran | Min Zhang | Vladimir Pervouchine
Proceedings of the 2010 Named Entities Workshop

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Whitepaper of NEWS 2010 Shared Task on Transliteration Generation
Haizhou Li | A Kumaran | Min Zhang | Vladimir Pervouchine
Proceedings of the 2010 Named Entities Workshop

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Report of NEWS 2010 Transliteration Mining Shared Task
A Kumaran | Mitesh M. Khapra | Haizhou Li
Proceedings of the 2010 Named Entities Workshop

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Whitepaper of NEWS 2010 Shared Task on Transliteration Mining
A Kumaran | Mitesh M. Khapra | Haizhou Li
Proceedings of the 2010 Named Entities Workshop

2009

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I2R’s machine translation system for IWSLT 2009
Xiangyu Duan | Deyi Xiong | Hui Zhang | Min Zhang | Haizhou Li
Proceedings of the 6th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we describe the system and approach used by the Institute for Infocomm Research (I2R) for the IWSLT 2009 spoken language translation evaluation campaign. Two kinds of machine translation systems are applied, namely, phrase-based machine translation system and syntax-based machine translation system. To test syntax-based machine translation system on spoken language translation, variational systems are explored. On top of both phrase-based and syntax-based single systems, we further use rescoring method to improve the individual system performance and use system combination method to combine the strengths of the different individual systems. Rescoring is applied on each single system output, and system combination is applied on all rescoring outputs. Finally, our system combination framework shows better performance in Chinese-English BTEC task.

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Efficient Beam Thresholding for Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of Machine Translation Summit XII: Posters

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A Source Dependency Model for Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of Machine Translation Summit XII: Posters

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Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)
Haizhou Li | A Kumaran
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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Report of NEWS 2009 Machine Transliteration Shared Task
Haizhou Li | A Kumaran | Vladimir Pervouchine | Min Zhang
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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Whitepaper of NEWS 2009 Machine Transliteration Shared Task
Haizhou Li | A Kumaran | Min Zhang | Vladimir Pervouchine
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering
Min Zhang | Haizhou Li
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Fast Translation Rule Matching for Syntax-based Statistical Machine Translation
Hui Zhang | Min Zhang | Haizhou Li | Chew Lim Tan
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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K-Best Combination of Syntactic Parsers
Hui Zhang | Min Zhang | Chew Lim Tan | Haizhou Li
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP
Keh-Yih Su | Jian Su | Janyce Wiebe | Haizhou Li
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Transliteration Alignment
Vladimir Pervouchine | Haizhou Li | Bo Lin
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Forest-based Tree Sequence to String Translation Model
Hui Zhang | Min Zhang | Haizhou Li | Aiti Aw | Chew Lim Tan
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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A Syntax-Driven Bracketing Model for Phrase-Based Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Topological Ordering of Function Words in Hierarchical Phrase-based Translation
Hendra Setiawan | Min-Yen Kan | Haizhou Li | Philip Resnik
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination
Boxing Chen | Min Zhang | Haizhou Li | Aiti Aw
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Keh-Yih Su | Jian Su | Janyce Wiebe | Haizhou Li
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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MARS: Multilingual Access and Retrieval System with Enhanced Query Translation and Document Retrieval
Lianhau Lee | Aiti Aw | Thuy Vu | Sharifah Aljunied Mahani | Min Zhang | Haizhou Li
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

2008

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I2R multi-pass machine translation system for IWSLT 2008.
Boxing Chen | Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we describe the system and approach used by the Institute for Infocomm Research (I2R) for the IWSLT 2008 spoken language translation evaluation campaign. In the system, we integrate various decoding algorithms into a multi-pass translation framework. The multi-pass approach enables us to utilize various decoding algorithm and to explore much more hypotheses. This paper reports our design philosophy, overall architecture, each individual system and various system combination methods that we have explored. The performance on development and test sets are reported in detail in the paper. The system has shown competitive performance with respect to the BLEU and METEOR measures in Chinese-English Challenge and BTEC tasks.

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The TALP&I2R SMT systems for IWSLT 2008.
Maxim Khalilov | Maria R. Costa-jussà | Carlos A. Henríquez Q. | José A. R. Fonollosa | Adolfo Hernández H. | José B. Mariño | Rafael E. Banchs | Chen Boxing | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper gives a description of the statistical machine translation (SMT) systems developed at the TALP Research Center of the UPC (Universitat Polite`cnica de Catalunya) for our participation in the IWSLT’08 evaluation campaign. We present Ngram-based (TALPtuples) and phrase-based (TALPphrases) SMT systems. The paper explains the 2008 systems’ architecture and outlines translation schemes we have used, mainly focusing on the new techniques that are challenged to improve speech-to-speech translation quality. The novelties we have introduced are: improved reordering method, linear combination of translation and reordering models and new technique dealing with punctuation marks insertion for a phrase-based SMT system. This year we focus on the Arabic-English, Chinese-Spanish and pivot Chinese-(English)-Spanish translation tasks.

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A Tree Sequence Alignment-based Tree-to-Tree Translation Model
Min Zhang | Hongfei Jiang | Aiti Aw | Haizhou Li | Chew Lim Tan | Sheng Li
Proceedings of ACL-08: HLT

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A Linguistically Annotated Reordering Model for BTG-based Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of ACL-08: HLT, Short Papers

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Exploiting N-best Hypotheses for SMT Self-Enhancement
Boxing Chen | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of ACL-08: HLT, Short Papers

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Regenerating Hypotheses for Statistical Machine Translation
Boxing Chen | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Linguistically Annotated BTG for Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Grammar Comparison Study for Translational Equivalence Modeling and Statistical Machine Translation
Min Zhang | Hongfei Jiang | Haizhou Li | Aiti Aw | Sheng Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Name Origin Recognition Using Maximum Entropy Model and Diverse Features
Min Zhang | Chengjie Sun | Haizhou Li | AiTi Aw | Chew Lim Tan | Xiaolong Wang
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Multi-View Co-Training of Transliteration Model
Jin-Shea Kuo | Haizhou Li
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Mining Transliterations from Web Query Results: An Incremental Approach
Jin-Shea Kuo | Haizhou Li | Chih-Lung Lin
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

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NIST 2007 Language Recognition Evaluation: From the Perspective of IIR
Haizhou Li | Bin Ma | Kong-Aik Lee | Khe-Chai Sim | Hanwu Sun | Rong Tong | Donglai Zhu | Changhuai You
Proceedings of the 22nd Pacific Asia Conference on Language, Information and Computation

2007

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A Statistical Language Modeling Approach to Lattice-Based Spoken Document Retrieval
Tee Kiah Chia | Haizhou Li | Hwee Tou Ng
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Semantic Transliteration of Personal Names
Haizhou Li | Khe Chai Sim | Jin-Shea Kuo | Minghui Dong
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Ordering Phrases with Function Words
Hendra Setiawan | Min-Yen Kan | Haizhou Li
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Learning Transliteration Lexicons from the Web
Jin-Shea Kuo | Haizhou Li | Ying-Kuei Yang
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Comparative Study of Four Language Identification Systems
Bin Ma | Haizhou Li
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 2, June 2006

2005

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Phrase-Based Statistical Machine Translation: A Level of Detail Approach
Hendra Setiawan | Haizhou Li | Min Zhang | Beng Chin Ooi
Second International Joint Conference on Natural Language Processing: Full Papers

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A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation
Min Zhang | Haizhou Li | Jian Su | Hendra Setiawan
Second International Joint Conference on Natural Language Processing: Full Papers

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A Phonotactic Language Model for Spoken Language Identification
Haizhou Li | Bin Ma
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Learning Phrase Translation using Level of Detail Approach
Hendra Setiawan | Haizhou Li | Min Zhang
Proceedings of Machine Translation Summit X: Papers

We propose a simplified Level Of Detail (LOD) algorithm to learn phrase translation for statistical machine translation. In particular, LOD learns unknown phrase translations from parallel texts without linguistic knowledge. LOD uses an agglomerative method to attack the combinatorial explosion that results when generating candidate phrase translations. Although LOD was previously proposed by (Setiawan et al., 2005), we improve the original algorithm in two ways: simplifying the algorithm and using a simpler translation model. Experimental results show that our algorithm provides comparable performance while demonstrating a significant reduction in computation time.

2004

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A Joint Source-Channel Model for Machine Transliteration
Haizhou Li | Min Zhang | Jian Su
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Direct Orthographical Mapping for Machine Transliteration
Min Zhang | Haizhou Li | Jian Su
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

1998

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Chinese Word Segmentation
Haizhou Li | Baosheng Yuan
Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation

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