Papers by Jianfeng Li

Exploring Conditional Variational Mechanism to Pinyin Input Method for Addressing One-to-Many Mappings in Low-Resource Scenarios (2024.acl-short)

Challenge:	Existing methods to build named entity recognition systems with limited labeled data are lacking.
Approach:	They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited.
Outcome:	The proposed NER systems outperform existing methods on few-shot and training-free settings.

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Bin Sun, Jianfeng Li, Hao Zhou, Fandong Meng, Kan Li, Jie Zhou

RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems (2021.acl-long)

Challenge:	Experimental results demonstrate the superior performance of our method.
Approach:	They propose to leverage conditional variational mechanism to simplify pinyin IME . they employ a strategy that facilitates interaction between pinyan and Chinese character information .
Outcome:	The proposed method improves the performance of pinyin input method engine (IME) under low-resource conditions.

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Baolin Peng, Chunyuan Li, Zhu Zhang, Chenguang Zhu, Jinchao Li, Jianfeng Gao

Enhancing Partially Relevant Video Retrieval with Robust Alignment Learning (2025.findings-emnlp)

Challenge:	Existing task-oriented dialog systems are less than satisfactory in robustness evaluation . existing systems are weak in robustity evaluation based on pre-training and fine-tuning .
Approach:	They propose to use a set of training examples to evaluate model generalization ability . they propose to include tasks with limited training data to favor models with strong generalization abilities .
Outcome:	The proposed model generalizes well with limited training data and is robust to user input across domains.

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Long Zhang, Peipei Song, Jianfeng Dong, Kun Li, Xun Yang

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)

Challenge:	Existing methods focus on enhancing multi-scale clip representations but lack robust data alignment . inherent data uncertainty renders PRVR vulnerable to distractor videos with spurious similarities .
Approach:	proposed framework for partially relevant video retrieval aims to retrieve untrimmed videos partially relevant to a given query.
Outcome:	The proposed framework can be seamlessly integrated into existing architectures.

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Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning (P18-1)

Challenge:	Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure.
Approach:	They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction.
Outcome:	The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models.

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Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)

Challenge:	Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users.
Approach:	They propose a framework that integrates planning for task-completion dialogue policy learning into a dialogue agent using a world model to mimic real user response and generate simulated experience.
Outcome:	The proposed framework integrates planning for task-completion dialogue policy learning with real user interaction and simulated user behavior.

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Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao

Semantic Text Processing

Challenge:	Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data.
Approach:	They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks.
Outcome:	The proposed model achieves new state-of-the-art on VAE language modeling benchmarks.

Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems (2020.acl-demos)

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Swadheen Shukla, Lars Liden, Shahin Shayandeh, Eslam Kamal, Jinchao Li, Matt Mazzola, Thomas Park, Baolin Peng, Jianfeng Gao

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	a wide variety of tasks have created a need for flexible task-oriented dialog systems . dialog flows are intuitively interpretable but lack the flexibility needed to handle complex dialogs .
Approach:	They propose a machine teaching tool for building dialog managers using familiar tools . they convert the dialog flow into a parametric model and use user-system dialog logs as training data .
Outcome:	The proposed tool combines the best of both approaches to build dialog managers . it converts the dialog flow into a parametric model and improves it over time .

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou

Summarization

Challenge:	Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions.
Approach:	They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization.
Outcome:	The proposed model outperforms the state-of-the-art models in the zero-shot directions.

CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)

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Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Kangwen Zhao, Dongyun Xue, Mingxiao Feng, Wengang Zhou, Houqiang Li

Neural Approaches to Conversational AI (P18-5)

Challenge:	Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases.
Approach:	They propose a feedback-driven iterative framework that leverages Large Language Models to generate initial test cases, execute them against known correct and incorrect solutions, and utilizes the failed results as feedback to guide the LLM in refining the test cases toward high fidelity and discriminability.
Outcome:	The proposed method outperforms the existing codecontests and codecontests+ models by 4.30% and 8.78%.

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Jianfeng Gao, Michel Galley, Lihong Li

Few-shot Natural Language Generation for Task-Oriented Dialog (2020.findings-emnlp)

Challenge:	This tutorial examines neural approaches to conversational AI that have been developed in the last few years.
Approach:	This tutorial presents a review of state-of-the-art neural approaches to conversational AI . they group conversational systems into question answering agents, task-oriented dialogue agents and social bots .
Outcome:	The present tutorial examines state-of-the-art approaches to conversational AI . it draws the connection between neural approaches and traditional symbolic approaches .

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Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning (D18-1)

Challenge:	Existing methods for NLG depend on heavily annotated data, which is infeasible for new domains.
Approach:	They propose a system that converts a dialog act into a response in natural language . they propose 'nuclear language generation' to simulate a few-shot learning setting .
Outcome:	The proposed model outperforms existing methods on a large set of annotated datasets.

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Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen

MindAgent: Emergent Gaming Interaction (2024.findings-naacl)

Challenge:	Existing approaches to improve the effectiveness and robustness of Deep Dyna-Q (DDQ) are based on a discriminator to control the quality of simulated experiences and to improve learning.
Approach:	They propose to use an RNN-based discriminator to control the quality of simulated experience to improve the effectiveness and robustness of Deep Dyna-Q.
Outcome:	The proposed framework outperforms DDQ by controlling the quality of simulated experience used for planning.

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Ran Gong, Qiuyuan Huang, Xiaojian Ma, Yusuke Noda, Zane Durante, Zilong Zheng, Demetri Terzopoulos, Li Fei-Fei, Jianfeng Gao, Hoi Vo

Learning to Adapt to Low-Resource Paraphrase Generation (2022.emnlp-main)

Challenge:	Large foundation models (LFMs) can perform complex scheduling in a multi-agent system and can coordinate agents to complete complex tasks that require extensive collaboration.
Approach:	They propose a gaming-based infrastructure that evaluates LFMs' planning and coordination capabilities in the context of gaming interaction.
Outcome:	The proposed infrastructure can be deployed in a customized VR version of Cuisineworld and adapted in the “Minecraft” domain.

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Zhigen Li, Yanmeng Wang, Rizhao Fan, Ye Wang, Jianfeng Li, Shaojun Wang

Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)

Challenge:	Conventional approaches to paraphrase generation often rely on a large number of parallel paraphrases, which require a lot of domain knowledge.
Approach:	They propose an adapter for paraphrase generation models optimized by meta-learning to overcome domain shifting problem when training on scarce labeled data.
Outcome:	The proposed model achieves state-of-the-art on three benchmark datasets.

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Jianfeng He, Runing Yang, Linlin Yu, Changbin Li, Ruoxi Jia, Feng Chen, Ming Jin, Chang-Tien Lu

GeoAgent: To Empower LLMs using Geospatial Tools for Address Standardization (2024.findings-acl)

Challenge:	Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods.
Approach:	They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets.
Outcome:	The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable.

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Chenghua Huang, Shisong Chen, Zhixu Li, Jianfeng Qu, Yanghua Xiao, Jiaxin Liu, Zhigang Chen

Semantic Text Processing

Challenge:	Existing approaches to address address standardization are lacking in the current field.
Approach:	They propose a framework that incorporates spatial knowledge into address texts and achieves efficient address standardization.
Outcome:	The proposed framework incorporates spatial knowledge into address texts and achieves efficient address standardization.

Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog (P19-1)

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Zhe Gan, Yu Cheng, Ahmed Kholy, Linjie Li, Jingjing Liu, Jianfeng Gao

Implicit Deep Latent Variable Models for Text Generation (D19-1)

Challenge:	Existing models for visual dialog infer the answer through multiple reasoning steps.
Approach:	They propose a model for visual dialog that uses multi-step reasoning to answer questions about an image.
Outcome:	The proposed model achieves a new state-of-the-art of 64.47% on the VisDial v1.0 dataset .

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Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, Changyou Chen

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)

Challenge:	Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons.
Approach:	They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior .
Outcome:	The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue.

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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li

Subgoal Discovery for Hierarchical Dialogue Policy Learning (D18-1)

Challenge:	Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios.
Approach:	They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings.
Outcome:	The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains.

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Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF (2026.acl-long)

Challenge:	Existing methods to develop dialogue agents for complex tasks require sparse reward signals.
Approach:	They propose a divide-and-conquer approach that exploits the hidden structure of a task . they use subgoals to divide a goal-oriented task into simpler subgoal sets .
Outcome:	The proposed approach performs competitively against state-of-the-art methods that require human-defined subgoals.

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Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Dongyun Xue, Wengang Zhou, Li Li, Houqiang Li

Challenge:	Existing approaches to tackling length bias are limited by their complexity or lack of a linear length-reward relation.
Approach:	They propose a framework that learns and corrects underlying bias patterns by fitting a length-reward relationship into a reward model.
Outcome:	The proposed framework improves length-controlled win rate and reduces verbosity without compromising performance.

Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (2024.findings-naacl)

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Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, Zhu Zhang

DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)

Challenge:	Existing methods for fact-checking text generated by large language models are expensive and time-consuming.
Approach:	They propose a plug-and-play framework that harnesses large language models for efficient fact-checking in a few-shot manner.
Outcome:	The proposed framework is compared with state-of-the-art models and shows that it can be used to speed up fact-checking in a few-shot manner.

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Yu Li, Baolin Peng, Pengcheng He, Michel Galley, Zhou Yu, Jianfeng Gao

Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training (2024.eacl-long)

Challenge:	Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data.
Approach:	They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
Outcome:	The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings.

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Jianfeng He, Julian Salazar, Kaisheng Yao, Haoqi Li, Jason Cai

Structure Trumps Size: Rethinking Data Quality for LLM Reasoning (2025.findings-emnlp)

Challenge:	End-to-end (E2E) spoken language understanding models are constrained by the cost of collecting speech-semantics pairs.
Approach:	They propose a model that learns E2E SLU without speech-semantics pairs . they propose cross-modal selective self-training (CMSST) to address imbalance and noise issues .
Outcome:	The proposed model learns E2E SLU without speech-semantics pairs . the proposed model requires the domains of speech-text and text-sensitization to match .

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Hu Xu, Zeyan Li, Rui Wang, Jianfeng Xu

MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)

Challenge:	Existing methods for fine-tuning Large Language Models rely on heuristic strategies and lack systematic, quantitative frameworks for evaluating data quality.
Approach:	They propose a multi-dimensional quantitative framework for reasoning data management . they rigorously evaluate and optimize datasets along six orthogonal dimensions .
Outcome:	The proposed framework rigorously evaluates and optimizes datasets along six orthogonal dimensions.

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Tingyi Zhang, Jiaan Wang, Zhixu Li, Jianfeng Qu, An Liu, Zhigang Chen, Hongping Zhi

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)

Challenge:	Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning.
Approach:	They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG.
Outcome:	The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation.

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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

A Survey on Cross-Lingual Summarization (2022.tacl-1)

Challenge:	ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets.
Approach:	They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation.
Outcome:	The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models.

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Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou

EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)

Challenge:	Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language.
Approach:	They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other .
Outcome:	The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis.

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Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu

Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching (2021.tacl-1)

Challenge:	Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values.
Approach:	They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction.
Outcome:	The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.

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Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, Jianfeng Gao

Budgeted Policy Learning for Task-Oriented Dialogue Systems (P19-1)

Challenge:	Existing methods for building task-oriented dialog systems are limited to a few tasks and domains.
Approach:	They propose a method that uses transfer learning and machine teaching to build task bots at scale.
Outcome:	The proposed method outperforms existing methods on well-studied task-oriented dialog benchmarks on well studied tasks.

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Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen

Guided Dialogue Policy Learning without Adversarial Learning in the Loop (2020.findings-emnlp)

Challenge:	Existing approaches to integrating reinforcement learning into task-oriented dialogue systems require a fixed, small amount of user interactions to learn.
Approach:	They propose a budget-conscious scheduling approach that optimizes a fixed, small amount of user interactions for dialogue agent learning.
Outcome:	The proposed approach improves on a movie-ticket booking task with simulated and real users.

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Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao

NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)

Challenge:	Reinforcement learning methods suffer from sparse and unstable reward signals . alternating training of dialogue agent and reward model can get stuck in local optima .
Approach:	They propose to decompose adversarial training into two steps to improve dialogue policy learning.
Outcome:	The proposed method achieves remarkable task success rate using both on-policy and off-poly reinforcement learning methods.

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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

Stand on The Shoulders of Giants: Building JailExpert from Previous Attack Experience (2025.emnlp-main)

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.

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Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, null Wangbaosheng

ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)

Challenge:	Existing methods to generate human-aligned content with a “jailbreak prompt” are inefficient and repetitive, causing inefficiency and a lack of experience.
Approach:	They propose a framework that integrates past attack experiences to aid current jailbreak attempts.
Outcome:	The proposed framework improves both attack effectiveness and efficiency compared to the current black-box jailbreak method.

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Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu, Jie Zhou

SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading (2024.emnlp-main)

Challenge:	Existing approaches to building cross-lingual summarization systems on dialogue documents are limited.
Approach:	They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Outcome:	The proposed model outperforms pipeline models on ClidSum and mDialBART.

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Tu Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues

Triplet-Free Knowledge-Guided Response Generation (2023.findings-acl)

Challenge:	Large Language Models (LLMs) are rapidly developing and are becoming more and more useful in scientific tasks.
Approach:	They propose to use LLM-as-a-judge to grade LLMs on SciEx to assess their ability on scientific tasks.
Outcome:	The proposed benchmarks show that the LLMs perform decently on free-form exams, achieving 0.948 Pearson correlation with expert grading.

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Dongming Li, Jianfeng Liu, Baoyuan Wang

Robust Navigation with Language Pretraining and Stochastic Sampling (D19-1)

Challenge:	Prior work focused on constructing ”latent” knowledge and learning how to ground it based on pseudo triplets.
Approach:	They propose to pretrain a response language model to measure relevance and consistency between any context and response and use search engines to collect top-ranked passages to serve as guiding knowledge without explicitly optimizing the ‘‘best’ latent knowledge.
Outcome:	The proposed model pretrains a response language model to measure relevance and consistency between any context and response, then uses search engines to collect the top-ranked passages to serve as the guiding knowledge without explicitly optimizing the ‘‘best’ latent knowledge.

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Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi

ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)

Challenge:	Existing methods to learn visual representations and action decoding schemes are limited to previously unseen instructions and environments.
Approach:	They propose a stochastic sampling scheme to reduce the gap between the expert actions in training and sampled actions in test to correct its own mistakes.
Outcome:	The proposed methods achieve 6% absolute gain over the previous best results on the Room-to-Room benchmark.

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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing (N19-1)

Challenge:	ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments.
Approach:	They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments.
Outcome:	The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments.

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Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin