Search Results for author:
Found 142 papers, 52 papers with code
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation
To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training
Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.
Ranked #5 on
Task-Oriented Dialogue Systems
on KVRET
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding
Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debias on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming. In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data. Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i. e., a kind of component in Chinese characters, during the training procedure. This consequently alleviates discriminative gender biases. Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution.
Finding the Dominant Winning Ticket in Pre-Trained Language Models
Empirically, we show that (a) the dominant winning ticket can achieve performance that is comparable with that of the full-parameter model, (b) the dominant winning ticket is transferable across different tasks, (c) and the dominant winning ticket has a natural structure within each parameter matrix.
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it.
Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation
Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings.
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation
(2) generate a post including selected products via the MGenNet (Multi-Generator Network).
Learning towards Selective Data Augmentation for Dialogue Generation
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator
The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
Scientific Paper Extractive Summarization Enhanced by Citation Graphs
We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.
Neural Machine Translation with Contrastive Translation Memories
Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios.
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
Spiking Neural Networks (SNNs) have been used for the implementation of Deep Neural Networks with superb energy efficiency on dedicated neuromorphic hardware, and recent years have witnessed increasing attention on combining SNNs with Reinforcement Learning, whereas most approaches still work with huge energy consumption and high latency.
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure
With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications.
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response.
Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation
These characteristics cannot be well handled by neural generation models that learn lyric-to-melody mapping in an end-to-end way, due to several issues: (1) lack of aligned lyric-melody training data to sufficiently learn lyric-melody feature alignment; (2) lack of controllability in generation to better and explicitly align the lyric-melody features.
Deep Spike Learning with Local Classifiers
We then propose two variants that additionally incorporate temporal dependencies through a backward and forward process, respectively.
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge.
Egocentric Video-Language Pretraining @ Ego4D Challenge 2022
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).
RetroGraph: Retrosynthetic Planning with Graph Search
We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e. g., AND-OR tree search, Monte Carlo tree search).
Ranked #1 on
Multi-step retrosynthesis
on USPTO-190
Egocentric Video-Language Pretraining
Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
Ranked #2 on
Action Recognition
on Charades-Ego
Target-aware Abstractive Related Work Generation with Contrastive Learning
The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
The experimental results show the proposed method achieves the state-of-the-art in terms of both accuracy and latency with promising energy preservation compared to ANNs.
OTExtSum: Extractive Text Summarisation with Optimal Transport
Optimal sentence extraction is conceptualised as obtaining an optimal summary that minimises the transportation cost to a given document regarding their semantic distributions.
Audio Deep Fake Detection System with Neural Stitching for ADD 2022
This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge\cite{Yi2022ADD}.
Time Domain Adversarial Voice Conversion for ADD 2022
In this paper, we describe our speech generation system for the first Audio Deep Synthesis Detection Challenge (ADD 2022).
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval
To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.
Learning to Express in Knowledge-Grounded Conversation
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue.
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress.
Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.
All in One: Exploring Unified Video-Language Pre-training
In this work, we for the first time introduce an end-to-end video-language model, namely \textit{all-in-one Transformer}, that embeds raw video and textual signals into joint representations using a unified backbone architecture.
Ranked #6 on
TGIF-Action
on TGIF-QA
Strategy Synthesis for Zero-sum Neuro-symbolic Concurrent Stochastic Games (Extended Version)
We present, for the first time, implementable value iteration and policy iteration algorithms to solve a class of uncountable state space CSGs, namely NS-CSGs, and prove their convergence.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling
Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question.
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition
This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities.
Video-Text Pre-training with Learned Regions
Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information.
Stylized Dialogue Generation with Multi-Pass Dual Learning
Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.
Object-aware Video-language Pre-training for Retrieval
In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations.
TaskDrop: A Competitive Baseline for Continual Learning of Sentiment Classification
In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i. e., a model is sequentially trained to classifier the sentiment of reviews of products in a particular category.
Target-Side Data Augmentation for Sequence Generation
Autoregressive sequence generation, a prevalent task in machine learning and natural language processing, generates every target token conditioned on both a source input and previously generated target tokens.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation
Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations
Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e. g., documents) when conversing with humans.
Point Cloud Registration using Representative Overlapping Points
3D point cloud registration is a fundamental task in robotics and computer vision.
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context.
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution
Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks.
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Based on this dataset, we propose a family of encoder-decoder models leveraging both textual and visual contexts, from coarse-grained image features extracted from CNNs to fine-grained object features extracted from Faster R-CNNs.
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement
In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.
Interactive Fusion of Multi-level Features for Compositional Activity Recognition
To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated.
Design and Commissioning of the PandaX-4T Cryogenic Distillation System for Krypton and Radon Removal
An online cryogenic distillation system for the removal of krypton and radon from xenon was designed and constructed for PandaX-4T, a highly sensitive dark matter detection experiment.
Instrumentation and Detectors High Energy Physics - Experiment
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling
Understanding neural models is a major topic of interest in the deep learning community.
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
Meaningful Answer Generation of E-Commerce Question-Answering
To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.
Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog
Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
We study knowledge-grounded dialogue generation with pre-trained language models.
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles
Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.
Ranked #2 on
Conversational Response Selection
on E-commerce
Social Adaptive Module for Weakly-supervised Group Activity Recognition
This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images
In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI.
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
Epipolar Transformers
The intuition is: given a 2D location p in the current view, we would like to first find its corresponding point p' in a neighboring view, and then combine the features at p' with the features at p, thus leading to a 3D-aware feature at p. Inspired by stereo matching, the epipolar transformer leverages epipolar constraints and feature matching to approximate the features at p'.
Ranked #1 on
3D Hand Pose Estimation
on InterHand2.6M
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation
Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.
Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.
Low-Resource Knowledge-Grounded Dialogue Generation
In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
Transformation of Dense and Sparse Text Representations
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders
In this work, we improve the WAE for response generation.
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.
RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).
Multilingual Dialogue Generation with Shared-Private Memory
Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.
Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing
There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.
Ranked #12 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Learning towards Abstractive Timeline Summarization
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
Ranked #1 on
Timeline Summarization
on MTS
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References
Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Ranked #10 on
Conversational Response Selection
on Douban
Mimicking Human Process: Text Representation via Latent Semantic Clustering for Classification
Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part.
A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
Under the framework, we simultaneously learn two matching models with independent training sets.
GSN: A Graph-Structured Network for Multi-Party Dialogues
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
Overcoming Catastrophic Forgetting via Model Adaptation
Several continual learning methods have been proposed to address the problem.
A Guider Network for Multi-Dual Learning
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
Product-Aware Answer Generation in E-Commerce Question-Answering
In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.
Ranked #1 on
Question Answering
on JD Product Question Answer
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).
Abstractive Text Summarization by Incorporating Reader Comments
To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.
Ranked #1 on
Reader-Aware Summarization
on RASG
Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations
To build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence.
Plan-And-Write: Towards Better Automatic Storytelling
Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.
On the Abstractiveness of Neural Document Summarization
Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries.
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording.
Iterative Document Representation Learning Towards Summarization with Polishing
In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.
Ranked #14 on
Extractive Text Summarization
on CNN / Daily Mail
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features.
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection
In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.
Modeling discourse cohesion for discourse parsing via memory network
Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance.
Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.
One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning
Automatic evaluating the performance of Open-domain dialogue system is a challenging problem.
Topic-Based Question Generation
In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Human-computer conversation systems have attracted much attention in Natural Language Processing.
Tree2Tree Learning with Memory Unit
Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.
Scale Up Event Extraction Learning via Automatic Training Data Generation
We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.
Style Transfer in Text: Exploration and Evaluation
Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
Ranked #5 on
Unsupervised Text Style Transfer
on Yelp
Diversifying Neural Conversation Model with Maximal Marginal Relevance
However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.
Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
The study on human-computer conversation systems is a hot research topic nowadays.
How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
Generative conversational systems are attracting increasing attention in natural language processing (NLP).
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision.
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification
For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
Neural Emoji Recommendation in Dialogue Systems
It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.
How Transferable are Neural Networks in NLP Applications?
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences.
Ranked #88 on
Natural Language Inference
on SNLI
Backward and Forward Language Modeling for Constrained Sentence Generation
Provided a specific word, we use RNNs to generate previous words and future words, either simultaneously or asynchronously, resulting in two model variants.
Automatic Subspace Learning via Principal Coefficients Embedding
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.
