Papers by Xing Niu
Chameleon LLMs: User Personas Influence Chatbot Personality Shifts (2025.emnlp-main)
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| Challenge: | Existing studies have examined whether large language models adapt their perceived personalities in response to user interactions. |
| Approach: | They propose to use a controlled simulation to measure chatbot personality shifts before and after the interaction to determine whether LLMs exhibit conversational adaptations. |
| Outcome: | The proposed model exhibits personality adaptations over prolonged interactions, while Emotional Stability and Intellect remain relatively stable. |
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation (2023.emnlp-main)
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Juan Pablo Zuluaga-Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, Marcello Federico
| Challenge: | Conventional speech-to-text translation systems are trained on single-speaker utterances, but they may not be applicable to real-life scenarios where the audio contains conversations by multiple speakers. |
| Approach: | They propose a speaker-turn-aware conversational speech translation model that integrates automatic speech recognition, speech translation and speaker turn detection using special tokens in a serialized labeling format. |
| Outcome: | The proposed model outperforms the reference systems on the multi-speaker condition while attaining comparable performance on the single-speakspeaker conditions. |
Multi-Task Neural Models for Translating Between Styles Within and Across Languages (C18-1)
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| Challenge: | Generating natural language requires conveying content in an appropriate style. |
| Approach: | They propose to solve two related tasks on generating text of varying formality jointly using multi-task learning. |
| Outcome: | The proposed models achieve state-of-the-art performance for formality transfer and formality-sensitive machine translation without training on style-annotated translation examples. |
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation (N19-1)
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| Challenge: | Existing work has addressed this problem by leveraging monolingual or multilingual data. |
| Approach: | They propose to introduce a differentiable reconstruction loss for neural machine translation to exploit the limited amounts of parallel text available in low-resource settings. |
| Outcome: | The proposed approach achieves small but consistent BLEU improvements on four language pairs in both translation directions and outperforms an alternative differentiable reconstruction strategy based on hidden states. |
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation (N19-1)
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| Challenge: | Existing approaches to correct exposure bias in machine translation are inadequate . scheduled sampling assumes that words are aligned at each time step . |
| Approach: | They propose a differentiable sampling algorithm that optimizes the probability that the reference can be aligned with the sampled output. |
| Outcome: | The proposed approach improves BLEU on translation tasks and is simpler to train with no sampling schedule. |
Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)
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| Challenge: | Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors. |
| Approach: | They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. |
| Outcome: | The proposed paradigm achieves integration intervention throughout model reasoning processes. |
Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | incorporating structure information can improve the performance of aspect-based sentiment analysis. |
| Approach: | They propose a method to conduct neuron-level manipulations on word representations in the frequency domain. |
| Outcome: | The proposed method can achieve or come close to state-of-the-art in ABSA. |
Benchmarking Query-Conditioned Natural Language Inference (2025.findings-acl)
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| Challenge: | Query-conditioned natural language inference (QC-NLI) is a new approach to detect inconsistencies in large language models. |
| Approach: | They propose a task of Query-Conditioned Natural Language Inference to determine the semantic relationship between two documents conditioned on a query. |
| Outcome: | The proposed task is based on a query-conditioned natural language inference (QC-NLI) it is used to determine the relationship between the premise and hypothesis given a given query. |
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation (2023.acl-short)
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| Challenge: | Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of outputs. |
| Approach: | They propose a new approach to attribute-controlled translation that leverages multilingual language models to perform ACT in few-shot and zero-shot settings. |
| Outcome: | The proposed approach improves generation accuracy over the standard prompting approach in both zero-shot and few-shot settings. |
Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Iterative Back-Translation and Dual Learning use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. |
| Approach: | They propose a dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. |
| Outcome: | The proposed method is more effective than Dual Learning on German-English and Turkish-English tasks. |
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality (2022.findings-naacl)
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| Challenge: | Specific problems arise when translating from English into languages with formality markers, such as “Are you sure?” . Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. |
| Approach: | They propose to train formality-controlled models by fine-tuning on labeled contrastive data and a metric to evaluate them. |
| Outcome: | The proposed model achieves high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality. |
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation (2022.emnlp-main)
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Anna Currey, Maria Nadejde, Raghavendra Reddy Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu
| Challenge: | Existing benchmarks have limited diversity in terms of gender phenomena, sentence structure, or language coverage. |
| Approach: | They propose a benchmark to evaluate gender accuracy in translation from English into eight widely-spoken languages. |
| Outcome: | The proposed benchmark provides realistic, gender-balanced, counterfactual data in eight language pairs where the gender of individuals is unambiguous in the input segment. |
Identifying Semantic Divergences in Parallel Text without Annotations (N18-1)
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| Challenge: | Parallel sentence pairs are sentences that are translations of each other and convey the same meaning in the source and target languages. |
| Approach: | They propose a model which detects meaning divergences in parallel sentence pairs . parallel sentence pair are translations of each other, therefore often assumed to convey the same meaning . |
| Outcome: | The proposed model detects divergences more accurately than models based on word alignments. |
Evaluating Robustness to Input Perturbations for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work has shown that Neural Machine Translation models are brittle to small perturbations in the input. |
| Approach: | They propose to use subword regularization to measure the relative degradation and changes in translation when perturbations are added to the input. |
| Outcome: | The proposed measures show that the models are more robust to perturbations when subword regularization methods are used. |
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation (2024.naacl-short)
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Benjamin Hsu, Xiaoyu Liu, Huayang Li, Yoshinari Fujinuma, Maria Nadejde, Xing Niu, Ron Litman, Yair Kittenplon, Raghavendra Pappagari
| Challenge: | Document translation is a challenge for machine translation systems that focus on textual content at the sentence level, ignoring global context and visual layout structure. |
| Approach: | They propose a benchmark dataset to evaluate document-level NMT systems . they use visual cues to preserve reading order and contiguous blocks of text . |
| Outcome: | The proposed benchmarks assess document-level NMT systems on the comprehensive task of translating semi-structured documents. |