Yue Feng (冯悦)

Hi! I'm a first-year Ph.D. student in the Department of Computer Science at University College London, affiliated with the Web Intelligence Group of the Centre for Artificial Intelligence. I am grateful to be advised by Prof. Emine Yilmaz and supported by a UKRI funded CDT in Foundational AI.

I aim to design systems that robustly and efficiently learn to understand human languages and web data to the end of advancing artificial intelligence web service and web information processing. My current research interests lie in natural language processing, information retrieval, machine learning, and data mining.

Feel free to reach me at yue [dot] feng [dot] 20 [at] ucl [dot] ac [dot] uk, or take my CV.

Education

Experience

Publications

1. A Reinforced Semi-supervised Neural Network for Helpful Review Identification Yue Feng, Miao Fan, Mingming Sun, and Ping Li In CIKM 2020 Abstract| Paper It is crucial to recommend helpful product reviews to consumers in e-commercial service, as the helpful ones can promote consumption. Existing methods for identifying helpful reviews are based on the supervised learning paradigm. The capacity of supervised methods, however, is limited by the lack of annotated reviews. In addition, there is a serious distributional bias between the labeled and unlabeled reviews. Therefore, this paper proposes a reinforced semi-supervised neural learning method (abbreviated as RSSNL) for helpful review identification, which can automatically select high-related unlabeled reviews to help training. Concretely, RSSNL composes with a reinforced unlabeled review selection policy and a semi-supervised pseudo-labeling review classifier. These two parts train jointly and integrate together based on the policy gradient framework. Extensive experiments on Amazon product reviews verify the effectiveness of RSSNL for using unlabeled reviews.

2. Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts. Jingyuan Zhang, Mingming Sun, Yue Feng, and Ping Li In ACL 2020 Abstract| Paper Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both English and Chinese datasets.

3. Teaching Machines to Extract Main Content for Machine Reading Comprehension. Zhaohui Li, Yue Feng, Jun Xu, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng In AAAI 2019 Abstract| Paper Machine reading comprehension, whose goal is to find answers from the candidate passages for a given question, has attracted a lot of research efforts in recent years. One of the key challenge in machine reading comprehension is how to identify the main content from a large, redundant, and overlapping set of candidate sentences. In this paper we propose to tackle the challenge with Markov Decision Process in which the main content identification is formalized as sequential decision making and each action corresponds to selecting a sentence. Policy gradient is used to learn the model parameters. Experimental results based on MSMARCO showed that the proposed model, called MC-MDP, can select high quality main contents and significantly improved the performances of answer span prediction.

4. From Greedy Selection to Exploratory Decision-Making: Diverse Ranking with Policy-Value Networks. Yue Feng, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng In SIGIR 2018 Abstract| Paper The goal of search result diversification is to select a subset of documents from the candidate set to satisfy as many different subtopics as possible. In general, it is a problem of subset selection and selecting an optimal subset of documents is NP-hard. Existing methods usually formalize the problem as ranking the documents with greedy sequential document selection. At each of the ranking position the document that can provide the largest amount of additional information is selected. It is obvious that the greedy selections inevitably produce suboptimal rankings. In this paper we propose to partially alleviate the problem with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP), referred to as M2Div. In M2Div, the construction of diverse ranking is formalized as an MDP process where each action corresponds to selecting a document for one ranking position. Given an MDP state which consists of the query, selected documents, and candidates, a recurrent neural network is utilized to produce the policy function for guiding the document selection and the value function for predicting the whole ranking quality. The produced raw policy and value are then strengthened with MCTS through exploring the possible rankings at the subsequent positions, achieving a better search policy for decision-making. Experimental results based on the TREC benchmarks showed that M2Div can significantly outperform the state-of-the-art baselines based on greedy sequential document selection, indicating the effectiveness of the exploratory decision-making mechanism in M2Div.

5. A Globalization-Semantic Matching Neural Network for Paraphrase Identification. Miao Fan, Wutao Lin, Yue Feng, Mingming Sun, and Ping Li In CIKM 2018 Abstract| Paper Paraphrase identification (PI) aims at determining whether two natural language sentences roughly have identical meaning. PI has been conventionally formalized as a binary classification task and widely used in many talks such as text summarization, plagiarism detection, etc. The emergence of deep neural networks (DNNs) renovates and dominates the learning paradigm of PI, as DNNs do not rely on lexical nor syntactic knowledge of a language, unlike traditional methods. State-of-the-art DNNs-based approaches to PI mainly adopt multi-layer convolutional neural networks (CNNs) to model paraphrastic sentences, which could discover alignments of phrases with the same length (unigram-to-unigram, bigram-to- bigram, trigram-to-trigram, etc.) at each layer. However, paraphras- ing phenomena globally exist at all levels of granularity between a pair of paraphrastic sentences, i.e., word-to-word, word-to-phrase, phrase-to-phrase, and even sentence-to-sentence. In this paper, we contribute a globalization-semantic matching neural network (GSMNN) paradigm which has been deployed in Baidu.com to tackle practical PI problems. Established on a weight- sharing single-layer CNN, GSMNN is composed of a multi-granular matching layer with the attention mechanism and a sentence-level matching layer. These layers are designed to capture essentially all phenomena of semantic matching. Evaluations are conducted on a public large-scale dataset for PI: Quora-QP which contains more than 400,000 paraphrasing and non-paraphrasing question pairs from Quora.com. Experimental results show that GSMNN outperforms the state-of-the-art model by a substantial margin.

6. FastInput: Improving Input Efficiency on Mobile Devices Jingyuan Zhang, Xin Wang, Yue Feng, Mingming Sun, and Ping Li In CIKM 2018 Abstract| Paper Mobile devices (e.g., smartphones) play a crucial role in our daily lives nowadays. People rely heavily on mobile devices for searching online, sending emails, chatting with friends, etc. As a result, input efficiency becomes increasingly important for real-time communication on mobile devices. Due to the small size of the screen on mobile devices, however, it is oftentimes frustrating for users to correct or update the input sequences on an even smaller input area on the screen. This often causes poor user experience. In this paper, we focus on improving the input efficiency on mobile devices to offer better user experience. In order to achieve efficient input, there are multiple challenges: 1) how to employ a single, unified representation of the keyboard layouts for different input languages; 2) how to build a framework to correct a mistouch immediately and predict the coming input texts (words or phrases) effectively; 3) how to deploy and evaluate the model on mobile devices with limited computational power. To address these challenges, we introduce FastInput to improve the user input efficiency on mobile devices. Three key techniques are developed in FastInput--layout modeling, instant mistouch correction and user input text prediction. We also design solutions for efficient deployment and evaluation of FastInput on mobile devices. The proposed FastInput achieves higher efficiency compared to the traditional input system over millions of user input sequences in different languages.

7. Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction. Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, and Ping Li In WSDM 2018 Abstract| Paper In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains more than forty thousand sentences and the corresponding facts in the SAOKE format labeled by crowd-sourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.

8. Multi-Task Neural Learning Architecture for End-to-End Identification of Helpful Reviews. Miao Fan, Yue Feng, Mingming Sun, Ping Li, Haifeng Wang, and Jianmin Wang In IEEE/ACM International Conferenceon Advances in Social Networks Analysis and Mining (ASONAM) 2018 Abstract| Paper Helpful reviews play a pivotal role in recommending desirable goods and accelerating purchase decisions of customers in e-commercial services. Given a large proportion of product reviews with unknown helpfulness/unhelpfulness, the research on automatic identification of helpful reviews has drawn much attention in recent years. However, state-of-the-art approaches still rely heavily on extracting heuristic text features from reviews with domain-specific knowledge. In this paper, we first introduce a multi-task neural learning (MTNL) architecture for identifying helpful reviews. The end-to-end neural architecture can learn to reconstruct effective features upon the raw input of words and even characters, and the multi-task learning paradigm helps to make more accurate predictions of helpful reviews based on a secondary task which fits the star ratings of reviews. We also build two datasets containing helpful/unhelpful reviews from different product categories in Amazon, and compare the performance of MTNL with several mainstream methods on both datasets. Experimental results confirm that MTNL outperforms the state-of-the-art approaches by a significant margin.

Honors & Award

• CIKM 2020 Student Travel Award.
• ACL 2020 Student Travel Award.
• AAAI 2019 Student Travel Award.
• SIGIR 2018 Student Travel Award.
• National Scholarship, top 0.2% in China, 2016.
• 1st Prize in National Information Security Contest, top 0.1% in China, 2015.
• Excellent Student Cadre Model, top 0.1% in Harbin Institute of Technology, 2014.