Search Results for author: Liang Pang

Found 33 papers, 19 papers with code

LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

1 code implementation25 Apr 2022 Yunchang Zhu, Liang Pang, Yanyan Lan, HuaWei Shen, Xueqi Cheng

Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be.

Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning

no code implementations6 Apr 2022 Shicheng Xu, Liang Pang, HuaWei Shen, Xueqi Cheng

It is because the end-to-end supervised learning on task-specific dataset makes model overemphasize the data sample bias and task-specific signals instead of the essential matching signals, which ruins the generalization of model to different tasks.

Information Retrieval Paraphrase Identification +2

Uncertainty Calibration for Ensemble-Based Debiasing Methods

no code implementations NeurIPS 2021 Ruibin Xiong, Yimeng Chen, Liang Pang, Xueqi Chen, Yanyan Lan

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target.

Fact Verification

Transductive Learning for Unsupervised Text Style Transfer

1 code implementation EMNLP 2021 Fei Xiao, Liang Pang, Yanyan Lan, Yan Wang, HuaWei Shen, Xueqi Cheng

The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future.

Style Transfer Text Style Transfer +1

Adaptive Information Seeking for Open-Domain Question Answering

1 code implementation EMNLP 2021 Yunchang Zhu, Liang Pang, Yanyan Lan, HuaWei Shen, Xueqi Cheng

Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus.

Open-Domain Question Answering

Toward the Understanding of Deep Text Matching Models for Information Retrieval

no code implementations16 Aug 2021 Lijuan Chen, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

We further extend these constraints to the semantic settings, which are shown to be better satisfied for all the deep text matching models.

Information Retrieval Semantic Text Matching +1

Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm

no code implementations12 Aug 2021 Lin Bo, Liang Pang, Gang Wang, Jun Xu, Xiuqiang He, Ji-Rong Wen

Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.

Document Ranking Information Retrieval +2

Sketch and Customize: A Counterfactual Story Generator

1 code implementation2 Apr 2021 Changying Hao, Liang Pang, Yanyan Lan, Yan Wang, Jiafeng Guo, Xueqi Cheng

In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending.

Text Generation

Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching

1 code implementation16 Jan 2021 Liang Pang, Yanyan Lan, Xueqi Cheng

However, these models designed for short texts cannot well address the long-form text matching problem, because there are many contexts in long-form texts can not be directly aligned with each other, and it is difficult for existing models to capture the key matching signals from such noisy data.

Community Question Answering Information Retrieval +3

Transformation Driven Visual Reasoning

1 code implementation CVPR 2021 Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i. e.~Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views).

Visual Question Answering Visual Reasoning +1

Beyond Language: Learning Commonsense from Images for Reasoning

1 code implementation Findings of the Association for Computational Linguistics 2020 Wanqing Cui, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng

This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP.

Language Modelling Question Answering

Modeling Topical Relevance for Multi-Turn Dialogue Generation

no code implementations27 Sep 2020 Hainan Zhang, Yanyan Lan, Liang Pang, Hongshen Chen, Zhuoye Ding, Dawei Yin

Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly.

Dialogue Generation

Ranking Enhanced Dialogue Generation

no code implementations13 Aug 2020 Changying Hao, Liang Pang, Yanyan Lan, Fei Sun, Jiafeng Guo, Xue-Qi Cheng

To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper.

Dialogue Generation Response Generation

Robust Reinforcement Learning with Wasserstein Constraint

no code implementations1 Jun 2020 Linfang Hou, Liang Pang, Xin Hong, Yanyan Lan, Zhi-Ming Ma, Dawei Yin

Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics.

reinforcement-learning

L2R2: Leveraging Ranking for Abductive Reasoning

1 code implementation22 May 2020 Yunchang Zhu, Liang Pang, Yanyan Lan, Xue-Qi Cheng

To fill this gap, we switch to a ranking perspective that sorts the hypotheses in order of their plausibilities.

Language Modelling Learning-To-Rank +1

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

2 code implementations12 Dec 2019 Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xue-Qi Cheng, Ji-Rong Wen

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.

Information Retrieval Learning-To-Rank

Continual Match Based Training in Pommerman: Technical Report

no code implementations18 Dec 2018 Peng Peng, Liang Pang, Yufeng Yuan, Chao GAO

We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones.

Continual Learning

Locally Smoothed Neural Networks

1 code implementation22 Nov 2017 Liang Pang, Yanyan Lan, Jun Xu, Jiafeng Guo, Xue-Qi Cheng

The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields.

Face Verification Question Answering +1

A Deep Investigation of Deep IR Models

no code implementations24 Jul 2017 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches.

Information Retrieval Learning-To-Rank

MatchZoo: A Toolkit for Deep Text Matching

1 code implementation23 Jul 2017 Yixing Fan, Liang Pang, Jianpeng Hou, Jiafeng Guo, Yanyan Lan, Xue-Qi Cheng

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.

Ad-Hoc Information Retrieval Information Retrieval +2

A Study of MatchPyramid Models on Ad-hoc Retrieval

1 code implementation15 Jun 2016 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng

Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it.

Machine Translation Paraphrase Identification +3

Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

1 code implementation15 Apr 2016 Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, Xue-Qi Cheng

In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i. e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position.

Text Matching as Image Recognition

7 code implementations20 Feb 2016 Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Xue-Qi Cheng

An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.

Ad-Hoc Information Retrieval Text Matching

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

1 code implementation26 Nov 2015 Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, Xue-Qi Cheng

Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.

Information Retrieval Question Answering +1

Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform

no code implementations27 Aug 2014 Yuyu Zhang, Liang Pang, Lei Shi, Bin Wang

This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014.

Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

no code implementations29 Nov 2013 Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.

Feature Engineering

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