Papers by Xinyu Hua
Sequentially Controlled Text Generation (2022.findings-emnlp)
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| Challenge: | Using GPT-2, long documents can ramble and do not follow human-like writing structure. |
| Approach: | They propose a controlled text generation task that generates documents with structure . they use a news article as a dataset to test different degrees of structural awareness . |
| Outcome: | The proposed task generates documents with a structure that is human-like, but long documents lack structure. |
Argument Generation with Retrieval, Planning, and Realization (P19-1)
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| Challenge: | a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives . |
| Approach: | They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia . |
| Outcome: | The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models. |
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation (2020.emnlp-main)
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| Challenge: | We present a content-controlled text generation framework for pre-trained Transformers . large pre-train models are the cornerstone of many state-of-the-art models in natural language understanding and generation tasks. |
| Approach: | They propose a content-controlled text generation framework that adds content planning to large pre-trained Transformers without modifying model architecture. |
| Outcome: | The proposed framework improves the quality of the outputs on three domains. |
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation (2021.acl-long)
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| Challenge: | Existing neural generation models fall short of coherence, thus requiring efficient content planning. |
| Approach: | They propose a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. |
| Outcome: | The proposed model outperforms competing models on argument generation and writing articles using New York Times’ Opinion section. |
Argument Mining for Understanding Peer Reviews (N19-1)
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| Challenge: | In 2015 alone, approximately 63.4 million hours were spent on peer reviews. |
| Approach: | They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types. |
| Outcome: | The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). |
Efficient Argument Structure Extraction with Transfer Learning and Active Learning (2022.findings-acl)
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| Challenge: | Identifying and understanding the argumentative discourse structure in text has been a critical task in argument mining. |
| Approach: | They propose a context-aware Transformer-based argument structure prediction model that outperforms models that rely on features or only encode limited contexts. |
| Outcome: | The proposed model outperforms models that rely on features or encode limited contexts on five domains and on peer reviews on five different domains. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
Neural Argument Generation Augmented with Externally Retrieved Evidence (P18-1)
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| Challenge: | Existing methods for generating arguments are limited to retrieval-based methods. |
| Approach: | They propose an encoder-decoder-based argument generation model enriched with externally retrieved evidence from Wikipedia. |
| Outcome: | The proposed model generates arguments with more topic-relevant content than current models based on automatic evaluation and human assessments on a large-scale dataset from reddit. |
OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference (2025.acl-long)
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Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang, Maosongcao Maosongcao, Jiaqi Wang, Weiyun Wang, Xinyu Fang, Wenhai Wang, Guangtao Zhai, Hua Yang, Haodong Duan, Kai Chen
| Challenge: | Existing open-source multi-modal large language models (MLLMs) focus on enhancing foundational capabilities, leaving a significant gap in human preference alignment. |
| Approach: | They propose a dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences. |
| Outcome: | The proposed dataset of 200K high-quality training samples improves human preference alignment while maintaining or enhancing performance on standard VQA benchmarks. |