Papers by Xiaodong Wu
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
Self-Taught Agentic Long Context Understanding (2025.acl-long)
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Yufan Zhuang, Xiaodong Yu, Jialian Wu, Ximeng Sun, Ze Wang, Jiang Liu, Yusheng Su, Jingbo Shang, Zicheng Liu, Emad Barsoum
| Challenge: | Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs. |
| Approach: | They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks. |
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
| Approach: | They propose to use a pre-trained language model to learn text representations first and then to constrain the scores with regression loss or ranking loss. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Automated Student Assessment Prize dataset. |
JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization (2022.emnlp-main)
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| Challenge: | e-commerce users express their needs using text, images, or videos . but detailed information provided by images is limited, and customer service systems cannot understand the intent of users without the input text. |
| Approach: | They construct a large-scale multimodal multi-turn dialogue dataset from a mainstream Chinese E-commerce platform . the dataset contains about 246K dialogue sessions, 3M utterances, and 507K images . |
| Outcome: | The proposed dataset contains 246K dialogue sessions, 3M utterances, 507K images . it also includes product knowledge bases and image category annotations . |
Agent Laboratory: Using LLM Agents as Research Assistants (2025.findings-emnlp)
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Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, Emad Barsoum
| Challenge: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Approach: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Outcome: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
Stabilizing Efficient Reasoning with Step-Level Advantage Selection (2026.findings-acl)
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| Challenge: | Large language models generate long and verbose reasoning traces at inference time . short context post-training alone induces substantial reasoning compression . |
| Approach: | They propose a step-level advantage selection approach that reduces reasoning length by over 30% . they propose to use GRPO without any length-aware objective to train models in a shorter context window . |
| Outcome: | The proposed approach reduces average reasoning length by over 30% while improving Pass@1 accuracy by 3.79 points over the strongest length-aware baseline. |
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)
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| Challenge: | Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based. |
| Approach: | They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation. |
| Outcome: | The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation . |
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)
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| Challenge: | Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators . |
| Approach: | They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world. |
| Outcome: | The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data . |
Reliable Use of Lemmas via Eligibility Reasoning and Section-Aware Reinforcement Learning (2026.acl-short)
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| Challenge: | Recent large language models (LLMs) perform strongly on mathematical benchmarks but often import conclusions without validating assumptions. |
| Approach: | They propose a model that encodes a lemma specification and trains with reinforcement learning and section-aware loss masking to assign penalty to the section responsible for errors. |
| Outcome: | The proposed model performs well on benchmarks but often misapplyes lemmas . the model is able to encode the specification and train with reinforcement learning . |
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |
Consistent Prototype Learning for Few-Shot Continual Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for few-shot continual relation extraction are overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle confusion of similar classes. |
| Approach: | They propose a few-shot continual relation extraction task that uses memory-enhanced modules to train a model on incrementally few-shot data to avoid forgetting old relations. |
| Outcome: | The proposed method outperforms existing methods on two commonly-used datasets. |
Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning (2026.findings-acl)
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Haiyang Yu, Yuchuan Wu, Fan Shi, Jinghui Lu, Ke Niu, Xiaodong Ge, Minghan Zhuo, Jingqun Tang, Bin Li
| Challenge: | Existing document benchmarks focus on English printed texts or simplified Chinese . current vision-language models struggle with visual complexity and poor adaptability . |
| Approach: | They propose a benchmark to evaluate Chinese ancient documents' visual/linguistic complexity . ancient documents are valuable cultural heritage, but they face challenges in digitization and understanding . |
| Outcome: | the first benchmark for Chinese ancient documents evaluates VLMs from OCR to knowledge reasoning . ancient documents carry thousands of years of Chinese history and culture . traditional methods only scan images, while current models struggle with visual complexity . |
Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT (2022.acl-long)
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| Challenge: | Transformer-based pre-trained models achieve state-of-the-art results, but they can be prohibitively costly. |
| Approach: | They propose a fine- and coarse-granularity hybrid self-attention that shortens the computational sequence length in self- attention by progressively shortening the computational time. |
| Outcome: | The proposed model reduces computation cost by shortening the computational sequence length in self-attention. |
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. |
| Approach: | They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks. |
| Outcome: | The proposed model significantly outperforms baselines across the board in e-commerce scenarios. |
No, you’re not alone: A better way to find people with similar experiences on Reddit (D19-55)
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| Challenge: | a probabilistic clustering algorithm can help users find posts that discuss experiences similar to their own . a recent study shows that probabilistic Clustering can yield a better performance than baseline clustering methods . |
| Approach: | They propose a probabilistic clustering algorithm that can help Reddit users find posts that discuss experiences similar to their own. |
| Outcome: | The proposed algorithm can find posts that discuss experiences similar to their own . it performs better than baseline clustering methods due to high runtime overhead . |
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing studies on joint entity and relation extraction fail to fully utilize the interdependence between entity types and relation types. |
| Approach: | They propose a synchronous dual network with cross-type attention via separately and interactively considering the entity types and relation types. |
| Outcome: | The proposed model achieves state-of-the-art on NYT and WebNLG datasets. |
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)
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| Challenge: | Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations . |
| Approach: | They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations. |
| Outcome: | The proposed model achieves comparable and even better results to strong baselines in terms of accuracy. |
Should I Believe in What Medical AI Says? A Chinese Benchmark for Medication Based on Knowledge and Reasoning (2025.acl-short)
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| Challenge: | Large language models (LLMs) generate hallucinations when handling unfamiliar information. |
| Approach: | They propose a Chinese benchmark to evaluate large language models' knowledge and reasoning capabilities in medication tasks. |
| Outcome: | The proposed benchmark evaluates models in indication, dosage and administration, contraindicated population, mechanisms of action, drug recommendation, and drug interaction across six datasets. |
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
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| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog (2022.findings-naacl)
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| Challenge: | Existing task-oriented dialog systems suffer from error propagation from inaccurate dialog states and responses. |
| Approach: | They propose a back reconstruction approach for end-to-end task-oriented dialog system . they use back reconstruction to reconstruct the original input context from the generated dialog states . |
| Outcome: | Extensive experiments on MultiWOZ 2.0 and CamRest676 show the effectiveness of the proposed model. |
EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)
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Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang
| Challenge: | Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules. |
| Approach: | They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration. |
| Outcome: | The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%. |
U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction (2023.tacl-1)
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| Challenge: | Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations. |
| Approach: | They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering. |
| Outcome: | The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE. |
MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering (2022.findings-emnlp)
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| Challenge: | Conventional HQA models retrieve coarse- or fine-grained evidence to reason the answer . however, they neglect a more general scenario requiring reasoning over heterogeneous data to answer a question. |
| Approach: | They propose a multi-granularity evidence retrieval and reasoning approach to answer questions over heterogeneous data using tables and passages linked to table cells. |
| Outcome: | The proposed approach significantly boosts the performance on the HybridQA dataset. |
Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge. |
| Approach: | They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality. |
| Outcome: | The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets. |
Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling (2026.findings-acl)
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| Challenge: | Existing studies on data marketplaces model static equilibria and complete information, which limits their realism. |
| Approach: | They propose an LLM-driven game-theoretic agent that makes equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. |
| Outcome: | The proposed framework matches equilibrium-consistent decision execution in a static data marketplace with a dynamic game tree memory and mechanism-guided reflection without updating parameters. |
PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training (2022.emnlp-main)
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| Challenge: | Existing studies focus on injecting noises into the input sequence, but feasibility of injecting them into the decoding sequence remains an open question. |
| Approach: | They propose a pre-training paradigm that integrates knowledge-enhanced decoding with noises in the prefix to strengthen the representation learning of entities that span over multiple input tokens. |
| Outcome: | The proposed model achieves state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains. |
TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games (2025.emnlp-main)
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| Challenge: | Recent advances in large reasoning models (LRMs) have driven significant breakthroughs across various reasoning tasks including deductive, arithmetic, commonsense, relational, and symbolic reasoning. |
| Approach: | They propose a programmatic approach to evaluate basic strategic, spatial, and logical reasoning abilities in large reasoning models through four two-player Tic-Tac-Toe-style games that humans can effortlessly solve from a young age. |
| Outcome: | The proposed model performs 41% lower on TTT-Bench than MATH 500 and AIME 2024 models, while the larger models perform better on longer reasoning traces. |
Compositional Mathematical Encoding for Math Word Problems (2023.findings-acl)
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| Challenge: | Existing MWP encoders work in a unimodal setting and map problem description to latent representation, then for decoding. |
| Approach: | They propose a Compositional Math Word Problem Solver which maps problem description to latent representation and decodes it in an interactive way. |
| Outcome: | Extensive experiments show that the proposed model outperforms state-of-the-art models on public benchmarks. |
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Experimental results show that MoNET outperforms previous DST methods in alleviating state momentum issues and improving the anti-noise ability. |
| Approach: | They propose to use previous state of each turn in training data as input to learn to predict current state. |
| Outcome: | The proposed model outperforms existing methods on multiWOZ datasets and shows that it can update and correct slot values and improve anti-noise ability. |
On the Faithfulness for E-commerce Product Summarization (2020.coling-main)
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| Challenge: | e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility . |
| Approach: | They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying . |
| Outcome: | The proposed model significantly improves the faithfulness of e-commerce product summarization tasks. |
Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (2025.coling-main)
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| Challenge: | Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context. |
| Approach: | They propose a dialog context fusion mechanism for end-to-end task-oriented dialog augmented with three additional tasks: dialog summarization, domain prediction, and slot detection. |
| Outcome: | The proposed method achieves state-of-the-art on the MultiWOZ and CrossWOZ datasets. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis (2022.acl-long)
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| Challenge: | Existing prompt tuning methods for cross-domain sentiment analysis have been underutilized due to domain discrepancy in the token distributions. |
| Approach: | They propose a new method to model cross-domain sentiment analysis using pre-trained language models by using soft prompts instead of hard templates. |
| Outcome: | The proposed method achieves state-of-the-art results on a publicly available sentiment analysis dataset. |
DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning (2026.findings-acl)
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Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun, Jialian Wu, Xiaodong Yu, Ze Wang, Chenliang Xu, Emad Barsoum, Zicheng Liu
| Challenge: | Multimodal large language models (MLLMs) have made rapid progress in perception and alignment, but their reasoning ability often lags behind strong text-only LLMs. |
| Approach: | They propose a method that transfers reasoning knowledge in the gradient space while preserving multimodal alignment. |
| Outcome: | Experiments on multimodal reasoning benchmarks show that DRIFT outperforms naive merging and standard SFT. |
RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint. |
| Approach: | They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk. |
| Outcome: | Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading. |
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product (2020.emnlp-main)
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| Challenge: | In the real world, product attribute values are incomplete and vary over time, which hinders practical applications. |
| Approach: | They propose a multimodal method to jointly predict product attributes and extract values from product images using multimodal product information. |
| Outcome: | The proposed method can predict product attributes and extract values from product images with the help of product images. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots (2022.coling-1)
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| Challenge: | Existing models for customer satisfaction prediction (CSP) focus on analyzing subjective customer satisfaction in conversational service, but they are hard to represent the important dynamic satisfaction states throughout the customer journey. |
| Approach: | They propose a model to track customer satisfaction in chatbots using a dialogue-level classification module to represent the dynamic satisfaction states at each turn. |
| Outcome: | The proposed model outperforms baselines and shows that it significantly outperformed multiple baselines. |
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying. |
| Approach: | They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one. |
| Outcome: | The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying . |
P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)
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| Challenge: | Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies. |
| Approach: | They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation. |
| Outcome: | Experiments on GLGE dataset show that P3LM improves on natural language generation tasks. |
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)
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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan
| Challenge: | Existing work generates long videos segment by segment sequentially, which is inefficient. |
| Approach: | They propose a Diffusion over Difference architecture for eXtremely Long video generation. |
| Outcome: | The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence. |
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking (2022.naacl-main)
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| Challenge: | Existing methods exploit the utterances of all dialogue turns to assign value to slots . this can lead to suboptimal results due to information introduced from irrelevant utterrances . |
| Approach: | They propose a SLot-TUrN Alignment enhanced approach to assign slot value . they explicitly align each slot with its most relevant utterance and then predict the corresponding value based on this aligned utteration. |
| Outcome: | The proposed approach achieves state-of-the-art on three multi-domain task-oriented dialogue datasets. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
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| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |
RevCore: Review-Augmented Conversational Recommendation (2021.findings-acl)
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| Challenge: | Existing conversational recommendation systems lack item information when conducted on short dialogue history and unfamiliar items. |
| Approach: | They propose a framework where reviews are seamlessly incorporated into conversational recommendation systems. |
| Outcome: | The proposed framework yields better performance on recommendation and conversation responding. |