Papers by Xiubo Geng
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting (2022.naacl-main)
Copied to clipboard
Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang
| Challenge: | Existing knowledge-grounded dialogue generation models only produce pedantic responses, which lacks emotion and attraction compared with the responses with polite style, positive and negative sentiments. |
| Approach: | They propose a method which generates responses via combing disentangled style templates and content templates. |
| Outcome: | The proposed method improves on evaluation metrics compared with state-of-the-art methods. |
Pre-training Cross-Modal Retrieval by Expansive Lexicon-Patch Alignment (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent large-scale vision-language pre-training relies on image-text global alignment by contrastive learning and is further boosted by fine-grained alignment in a weakly contrastive manner for cross-modal retrieval. |
| Approach: | They propose expansive lexicon-patch alignment (ELA) to align image patches with a vocabulary rather than only the words explicitly in the text for annotation-free alignment and information augmentation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on cross-modal retrieval and can learn representative fine-grained information. |
ADAM: Dense Retrieval Distillation with Adaptive Dark Examples (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to retrieve data from multiple encoders are too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student. |
| Approach: | They propose a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with adaptive dark examples. |
| Outcome: | The proposed framework can better transfer the dark knowledge held in the teacher with adaptive dark examples. |
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to learning sentence embeddings in unsupervised manner depend on mono-augmenting . existing approaches depend on augmenting biases and thus corrupt the quality of sentence embeds. |
| Approach: | They propose a method to augment a sentence with a semantically-close positive instance to construct contrastive pairs in unsupervised manner. |
| Outcome: | The proposed method improves performance on STS benchmarks and compares with existing methods. |
Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)
Copied to clipboard
| Challenge: | Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data. |
| Approach: | They propose a framework that facilitates information refinement through synergy between RMs and LLMs. |
| Outcome: | The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks. |
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (2022.acl-long)
Copied to clipboard
| Challenge: | Existing work on event-centric reasoning fails to model event-level correlations . Existing studies limit their scope to specific scenarios or overlook event- level correlations. |
| Approach: | They propose to pre-train a general Correlation-aware context-to-Event Transformer for event-centric reasoning by highlighting event-level correlations with effective training. |
| Outcome: | The proposed model is applicable to a wide range of event-centric reasoning scenarios, considering its versatility of event correlation types, application formulations, and reasoning types. |
Reasoning over Entity-Action-Location Graph for Procedural Text Understanding (2021.acl-long)
Copied to clipboard
| Challenge: | Procedural text understanding aims at tracking the states and locations of entities mentioned in a paragraph. |
| Approach: | They propose a framework to model entities-entity, action, and location relations using a graph neural network. |
| Outcome: | The proposed approach outperforms strong baselines on two datasets, ProPara and Recipes. |
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base (D19-1)
Copied to clipboard
| Challenge: | Recent approaches to handle large knowledge base decompose tasks into subtasks and solve them sequentially. |
| Approach: | They propose a multi-task learning framework that resolves coreference in conversations . they propose enabling shared supervisions and type-aware entity detection model . |
| Outcome: | The proposed framework improves overall F1 score from 67% to 79% on a large-scale conversational question answering dataset. |
CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
| Approach: | They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds. |
| Outcome: | The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications. |
Multimodal Dialogue Response Generation (2022.acl-long)
Copied to clipboard
Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang
| Challenge: | Existing studies focus on multimodal dialogue models but neglect generation methods. |
| Approach: | They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain. |
| Outcome: | Experiments show that the proposed model can generate informative text and high-resolution image responses. |
Social Norms-Grounded Machine Ethics in Complex Narrative Situation (2022.coling-1)
Copied to clipboard
| Challenge: | Recent studies focus on data-driven methods to judge the ethics of complex real-world narratives but face two major challenges: they cannot handle dilemma situations due to a lack of basic knowledge about social norms; and they focus on sparse situation-level judgment regardless of the social norm. |
| Approach: | They propose to complement a complex situation with grounded social norms by a norm-supported ethical judgment model in line with neural module networks to alleviate dilemma situations and improve norm-level explainability. |
| Outcome: | The proposed model improves state-of-the-art performance on two narrative judgment benchmarks. |
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to build labeled training data from domain-specific data are expensive to obtain. |
| Approach: | They propose a Prompt-based Data Augmentation model which only trains small-scale Soft Promptes in frozen Pre-trained Language Models. |
| Outcome: | The proposed model outperforms several baseline models on four benchmarks and is complementary with unlabeled in-domain data. |
FAA: Fine-grained Attention Alignment for Cascade Document Ranking (2023.acl-long)
Copied to clipboard
| Challenge: | Contemporary document ranking methods focus on transforming documents into passages to handle long inputs, but intensive query-irrelevant content may lead to harmful distraction and high query latency. |
| Approach: | They propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model. |
| Outcome: | Experiments on MS MARCO and TREC DL show that the proposed method is effective in document ranking tasks. |
Towards Interpretable Reasoning over Paragraph Effects in Situation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models ignore complex reasoning process and solve it with a one-step "black box" approach. |
| Approach: | They propose a sequential approach which explicitly models each step of the reasoning process with neural network modules. |
| Outcome: | The proposed model is more interpretable and more accurate than existing models. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events . |
| Approach: | They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate. |
| Outcome: | The proposed model is compared with baselines using external KG or not on a script reasoning task. |
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)
Copied to clipboard
| Challenge: | Generating natural and informative texts has been a long-standing problem in NLP. |
| Approach: | They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework. |
| Outcome: | The proposed model can learn what and how to generate on two text generation tasks. |
Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)
Copied to clipboard
Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang
| Challenge: | Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue. |
| Approach: | They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates. |
| Outcome: | The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets. |
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding (2022.naacl-main)
Copied to clipboard
| Challenge: | Temporal reading comprehension (TRC) is a natural way to study temporal relations since natural language questions are flexible to capture divergent temporal relationships. |
| Approach: | They propose a reading comprehension approach that uses precise question understanding . they embed a temporal ordering question into two vectors and evaluate the temporal relation based on that . |
| Outcome: | The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. |
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to solve multilingual question answering over knowledge graph (KGQA) use of bilingual lexicon induction to map training questions into those in target language circumvents language inconsistency . |
| Approach: | They propose to use bilingual lexicon induction to map training questions in source and target languages as augmented training data to minimize syntax-disorder. |
| Outcome: | The proposed model narrows the gap in zero-shot cross-lingual transfer between source and target languages. |
Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world. |
| Approach: | They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge . |
| Outcome: | The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation. |
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models for multi-party conversation represent interlocutors and utterances individually . existing methods ignore complicated structure of MPC which may provide crucial interlocutor and tertiary semantics. |
| Approach: | They propose a pre-trained model for multi-party conversation that considers learning who says what to whom in a unified model with elaborated self-supervised tasks. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks at two benchmarks. |
Towards Robust Ranker for Text Retrieval (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for text retrieval are based on a 'retrieval & rerank' pipeline, which uses a fast retriever to fetch a set of top document candidates, while a robust ranker is based upon a weak negative mining during contrastive learning. |
| Approach: | They propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker. |
| Outcome: | The proposed model outperforms the existing de facto ranker training paradigms on the passage retrieval benchmarks using BM25-reranking, full-ranking and retriever distillation. |
Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)
Copied to clipboard
Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang
| Challenge: | Recent advances in neural models have shown promising progress on this task, but key challenges remain . |
| Approach: | They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial. |