Papers by Nan Jiang

Can Language Models Replace Programmers for Coding? REPOCOD Says ‘Not Yet’ (2025.acl-long)

Challenge:	Existing approaches for inferential text generation ignore context that is not explicitly provided . Existing models ignore background knowledge that provides crucial evidence to generate inferences .
Approach:	They propose an approach that automatically finds evidence for an event from a large text corpus and leverages it to guide the generation of inferential texts.
Outcome:	The proposed model generates inferential texts from a large text corpus and uses evidence to guide it.

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Shanchao Liang, Nan Jiang, Yiran Hu, Lin Tan

Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)

Challenge:	Existing benchmarks for code generation use short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks.
Approach:	They propose a Python code-generation benchmark that contains 980 whole-function generation tasks with realistic dependencies from 11 popular projects.
Outcome:	The proposed benchmarks are short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks.

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Haoyang Huang, Yaobo Liang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Ming Zhou

CoSQA: 20,000+ Web Queries for Code Search and Question Answering (2021.acl-long)

Challenge:	Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages.
Approach:	They propose to use Unicoder to train models in one language and apply it to other languages.
Outcome:	Experiments show that Unicoder learns the mappings among different languages from more perspectives.

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Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan

Information Retrieval

Challenge:	Using deep neural networks to find codes is difficult . we present a dataset that includes 20,604 labels for natural language queries and codes .
Approach:	They introduce a contrastive learning method to enhance text-code matching . they find that CoSQA improves the accuracy of code question answering by 5.1% .
Outcome:	The proposed method improves the accuracy of code question answering by 5.1% and improves by 10.5% on a CodeBERT model.

Benchmarking Data Science Agents (2024.acl-long)

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Yuge Zhang, Qiyang Jiang, XingyuHan XingyuHan, Nan Chen, Yuqing Yang, Kan Ren

MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have emerged as promising data science aids, assisting humans in data analysis and processing.
Approach:	They propose an evaluation paradigm and benchmarks that assess the performance of data science agents throughout the entire data science lifecycle.
Outcome:	The proposed evaluation paradigm streamlines dataset preparation, improves coverage, and expands benchmarking comprehensiveness.

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Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Current benchmarks focus on coarse-grained knowledge, leaving the intricacies of fine-grounded knowledge unexplored.
Approach:	They propose a benchmark and dataset specifically designed for FG multimodal entity knowledge editing.
Outcome:	The proposed benchmark underscoring the complexity of FG knowledge editing in MLLMs.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Fei Yuan, Linjun Shou, Xuanyu Bai, Ming Gong, Yaobo Liang, Nan Duan, Yan Fu, Daxin Jiang

Multilinguality

Challenge:	Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data.
Approach:	They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs.
Outcome:	The proposed task improves on two cross-lingual MRC datasets.

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)

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Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan

Responsible & Trustworthy NLP

Challenge:	Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process.
Approach:	They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information.
Outcome:	The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA.

MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media (2025.emnlp-main)

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Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu

Responsible & Trustworthy NLP

Challenge:	Social media is a key platform for emotional expression, yet deep learning lacks flexibility and interpretability.
Approach:	They propose to use Chinese social media to train interpretable mental health instruction datasets to test models' ability to explain their decisions.
Outcome:	The proposed models outperform deep learning and LLMs on three mental health downstream tasks and demonstrate their potential for clinical applications.

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan

Natural Language Interfaces

Challenge:	Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach:	They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome:	The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.

No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension (2020.findings-emnlp)

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Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, Daxin Jiang

Reasoning

Challenge:	Natural Questions (NQ) benchmark sets new challenges for machine reading comprehension.
Approach:	They propose a novel approach to handle all answer types systematically using a two-step training procedure.
Outcome:	The proposed approach achieved the top 1 on both long and short answer leaderboards with F1 scores of 77.2 and 64.1.

Code Execution with Pre-trained Language Models (2023.findings-acl)

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Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan

WAFFLE: Fine-tuning Multi-Modal Model for Automated Front-End Development (2025.acl-long)

Challenge:	Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution.
Approach:	They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models.
Outcome:	The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation.

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Shanchao Liang, Nan Jiang, Shangshu Qian, Lin Tan

xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering (2021.acl-long)

Challenge:	Large Language Models (LLMs) have shown promise in generating source code, but two major challenges persist in UI-to-HTML code generation: (1) effectively representing HTML’s hierarchical structure for LLMs; and (2) bridging the gap between the visual nature of UI designs and the text-based format of HTML code.
Approach:	They propose a structure-aware attention mechanism that uses a contrastive fine-tuning approach to align LLMs’ understanding of UI images and HTML code.
Outcome:	The proposed model outperforms existing methods on the WebSight-Test and Design2Code benchmarks.

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Nan Yang, Furu Wei, Binxing Jiao, Daxing Jiang, Linjun Yang

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base (D19-1)

Challenge:	Existing approaches to find relevant passages using sparse keywords are not effective for open domain question answering.
Approach:	They propose a new contrastive learning method for learning a dual-encoder model for question-passage matching using a large pool of negative samples.
Outcome:	The proposed method maintains large pool of negative samples and optimizes question-to-passage and passage-to question matching tasks.

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Tao Shen, Xiubo Geng, Tao Qin, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, Daxin Jiang

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)

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.

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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan

Multi-View Document Representation Learning for Open-Domain Dense Retrieval (2022.acl-long)

Challenge:	Existing models for pre-training are not convenient for users to find and set them up.
Approach:	They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model .
Outcome:	The proposed models achieve new state-of-the-art on 10 benchmarks.

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Shunyu Zhang, Yaobo Liang, Ming Gong, Daxin Jiang, Nan Duan

Representation Learning

Challenge:	Existing methods for dense retrieval are hard to match with multiple views.
Approach:	They propose a multi-view document representation learning framework to generate multiple embeddings through viewers to represent documents and enforce them to align with different queries.
Outcome:	The proposed method outperforms recent works and achieves state-of-the-art results.

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach (2020.findings-emnlp)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

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Maosen Zhang, Nan Jiang, Lei Li, Yexiang Xue

Text Generation

Challenge:	Generating natural language under complex constraints is a principled formulation towards controllable text generation.
Approach:	They propose a method to specify combinatorial constraints for sentence generation . they use a tree search algorithm embedded into the proposal process of the Markov Chain Monte Carlo .
Outcome:	The proposed method achieves consistent and significant improvement on multiple language generation tasks.

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis (2020.findings-emnlp)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Huaishao Luo, Lei Ji, Tianrui Li, Daxin Jiang, Nan Duan

Sentiment Analysis

Challenge:	Existing studies ignore aspect terms interaction when labeling polarities . aspect terms extraction and aspect sentiment classification are two fundamental tasks .
Approach:	They propose a GRadient hArmonized and CascadEd labeling model to solve the imbalance issue . they extend the gradient harmonized mechanism used in object detection to aspect-based sentiment analysis .
Outcome:	The proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.

RikiNet: Reading Wikipedia Pages for Natural Question Answering (2020.acl-main)

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Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Nan Duan

Natural Language Interfaces

Challenge:	Using Wikipedia pages to answer open-domain questions remains challenging in natural language understanding.
Approach:	They propose a model which reads Wikipedia pages for natural question answering . it uses a dynamic paragraph dual-attention reader and a cascaded answer predictor .
Outcome:	The proposed model outperforms the human model on the Natural Questions dataset . it achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks .

CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)

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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Large pre-trained models have improved performance on a variety of natural language processing tasks.
Approach:	They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators.
Outcome:	The proposed model performs better on two NL-PL applications by fine-tuning model parameters.

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Multilinguality

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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

Multilinguality

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.

Syntax-Enhanced Pre-trained Model (2021.acl-long)

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Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan

Syntactic Text Processing

Challenge:	Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages.
Approach:	They propose a model that utilizes the syntactic structure of text in pre-training and fine-tuning stages.
Outcome:	The proposed model achieves state-of-the-art on six public benchmark datasets.

LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics (N18-1)

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Zhen Xu, Nan Jiang, Bingquan Liu, Wenge Rong, Bowen Wu, Baoxun Wang, Zhuoran Wang, Xiaolong Wang

Dialogue Systems & Conversational Agents

Challenge:	Existing evaluation metrics for NRG models can't measure semantic relevance and diversity of generated results.
Approach:	They propose a large-scale domain-specific conversational corpus with preprocessing and cleansing procedures for model training and a testing set for measuring the diversity of generated results.
Outcome:	The proposed corpus can be taken as a new benchmark dataset for the NRG task.

Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)

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Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, Nan Duan

Structured Data in NLP

Challenge:	a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction.
Approach:	They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction .
Outcome:	The proposed model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei

CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)

Challenge:	SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training.
Approach:	They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning.
Outcome:	The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines.

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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan

Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation (2022.findings-emnlp)

Challenge:	Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context.
Approach:	They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus.
Outcome:	The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity.

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Xiaonan Li, Daya Guo, Yeyun Gong, Yun Lin, Yelong Shen, Xipeng Qiu, Daxin Jiang, Weizhu Chen, Nan Duan

DART: Distilling Autoregressive Reasoning to Silent Thought (2025.emnlp-main)

Challenge:	Existing methods for contrastive pre-training ignore the relevance between codes in large code corpus.
Approach:	They propose a Soft-labeled contrastive pre-training framework with positive sample construction methods to learn functional-level code representation.
Outcome:	The proposed framework can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation.

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Nan Jiang, Ziming Wu, De-Chuan Zhan, Fuming Lai, Shaobing Lian

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)

Challenge:	Existing models that use Chain-of-Thought (CoT) have been slow to deploy in real-time applications due to its autoregressive nature.
Approach:	They propose a framework that replaces autoregressive CoT with non-autoregressive Silent Thought (ST) the framework uses a lightweight Reasoning Evolvement Module to align hidden states with the CoT pathway and a Reasoning Embedment Module (REM) during inference, only the ST pathway is activated, enabling the ST tokens to evolve into informative embeddings.
Outcome:	The proposed framework replaces autoregressive CoT with non-autoregressive Silent Thought (ST) it enables LLMs to generate answers directly from ST tokens without additional computational cost .

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Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation (2020.emnlp-main)

Challenge:	Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies.
Approach:	They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature .
Outcome:	The proposed framework outperforms existing systems at long and short answer criteria.

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Dayiheng Liu, Yeyun Gong, Yu Yan, Jie Fu, Bo Shao, Daxin Jiang, Jiancheng Lv, Nan Duan

Text Generation

Challenge:	Existing methods for news headline generation focus on producing a single short sentence . et al., 2017; Gehrmann e.t., 2018; Zhong ee., 2019) focus on single-headline generation.
Approach:	They propose a method to generate multiple headlines with keyphrases of user interests . they propose generating multiple keyphrase-relevant headlines using a transformer decoder .
Outcome:	The proposed method achieves state-of-the-art in terms of quality and diversity.

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)

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Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou

Challenge:	Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments.
Approach:	They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts.
Outcome:	The proposed method achieves superior performance on a large dataset for propaganda detection.

Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA (2022.findings-emnlp)

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Junjie Huang, Wanjun Zhong, Qian Liu, Ming Gong, Daxin Jiang, Nan Duan

Allies: Prompting Large Language Model with Beam Search (2023.findings-emnlp)

Challenge:	Existing methods to retrieve evidences from corpus are difficult due to table-text discrepancy and data sparsity problem.
Approach:	They propose an optimized OpenQA Table-Text Retriever to retrieve tabular and textual evidences from tabular resources.
Outcome:	The proposed OpenQA Table-Text Retriever significantly outperforms existing methods on QA tasks.

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Hao Sun, Xiao Liu, Yeyun Gong, Yan Zhang, Daxin Jiang, Linjun Yang, Nan Duan