Search Results for author:
Found 153 papers, 58 papers with code
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots
In this work, we investigate the problem of satisfaction states tracking and its effects on CSP in E-commerce service chatbots.
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization
It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Ranked #11 on
Abstractive Text Summarization
on CNN / Daily Mail
(using extra training data)
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline
Since there is no large number of public Chinese tables, we also collect a large-scale, multi-domain tabular corpus to facilitate future Chinese table pre-training, which includes one million tables and related natural language text with auxiliary supervised interaction signals.
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
OPERA: Operation-Pivoted Discrete Reasoning over Text
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service
There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
A Group Fairness Lens for Large Language Models
The rapid advancement of large language models has revolutionized various applications but also raised crucial concerns about their potential to perpetuate biases and unfairness when deployed in social media contexts.
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals.
Leveraging Label Information for Multimodal Emotion Recognition
Finally, we devise a novel label-guided attentive fusion module to fuse the label-aware text and speech representations for emotion classification.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design.
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition
Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation
Empathy is a crucial factor in open-domain conversations, which naturally shows one's caring and understanding to others.
Learning to Generate Poetic Chinese Landscape Painting with Calligraphy
In this paper, we present a novel system (denoted as Polaca) to generate poetic Chinese landscape painting with calligraphy.
Roto-Translation Invariant Formation of Fixed-Wing UAVs in 3D: Feasibility and Control
Secondly, given feasible formations, we design a formation controller by introducing a virtual leader and employing the compensation of rotation, followed by proving the stability of the closed-loop system.
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation
The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding
Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair.
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking
Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values.
UFO2: A unified pre-training framework for online and offline speech recognition
In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows for online and offline modes into one process, and 2) improves the Word Error Rate (WER) performance with limited utterance annotating.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
P$^3$LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training
Conventional autoregressive left-to-right (L2R) sequence generation faces two issues during decoding: limited to unidirectional target sequence modeling, and constrained on strong local dependencies.
MuGER$^2$: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering
To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER$^2$, a Multi-Granularity Evidence Retrieval and Reasoning approach.
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog
Traditional end-to-end task-oriented dialog systems first convert dialog context into belief state and action state before generating the system response.
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation
Question answering requiring discrete reasoning, e. g., arithmetic computing, comparison, and counting, over knowledge is a challenging task.
AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL
This study investigates the task of knowledge-based question generation (KBQG).
Composable Text Controls in Latent Space with ODEs
This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
Ranked #2 on
Unsupervised Text Style Transfer
on Yelp
Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks
This paper introduces a novel neural network - flow completion network (FCN) - to infer the fluid dynamics, includ-ing the flow field and the force acting on the body, from the incomplete data based on Graph Convolution AttentionNetwork.
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog
To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response.
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking
Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history.
OPERA:Operation-Pivoted Discrete Reasoning over Text
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
The low-entropy hydration shell at the binding site of spike RBD determines the contagiousness of SARS-CoV-2 variants
The low-entropy level of hydration shells at the binding site of a spike protein is found to be an important indicator of the contagiousness of the coronavirus.
Label Anchored Contrastive Learning for Language Understanding
Motivated by this, we propose a novel label anchored contrastive learning approach (denoted as LaCon) for language understanding.
SE-GAN: Skeleton Enhanced GAN-based Model for Brush Handwriting Font Generation
There is rare research on brush handwriting font generation, which involves holistic structure changes and complex strokes transfer.
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue
Turn-taking, aiming to decide when the next speaker can start talking, is an essential component in building human-robot spoken dialogue systems.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis
Building Spoken Language Understanding (SLU) robust to Automatic Speech Recognition (ASR) errors is an essential issue for various voice-enabled virtual assistants.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT
To confront this, we propose FCA, a fine- and coarse-granularity hybrid self-attention that reduces the computation cost through progressively shortening the computational sequence length in self-attention.
Space Layout of Low-entropy Hydration Shells Guides Protein Binding
According to an analysis of determined protein complex structures, shape matching between the largest low-entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a regular pattern.
Cross-modal Contrastive Distillation for Instructional Activity Anticipation
In this study, we aim to predict the plausible future action steps given an observation of the past and study the task of instructional activity anticipation.
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.
ViDA-MAN: Visual Dialog with Digital Humans
We demonstrate ViDA-MAN, a digital-human agent for multi-modal interaction, which offers realtime audio-visual responses to instant speech inquiries.
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
We present the solutions of top-5 teams participating in the JDDC multimodal dialogue challenge based on this dataset, which provides valuable insights for further researches on the multimodal dialogue task.
RoR: Read-over-Read for Long Document Machine Reading Comprehension
To address this problem, we propose RoR, a read-over-read method, which expands the reading field from chunk to document.
CUSTOM: Aspect-Oriented Product Summarization for E-Commerce
To address the problem, we propose CUSTOM, aspect-oriented product summarization for e-commerce, which generates diverse and controllable summaries towards different product aspects.
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System
Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e. g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation.
RevCore: Review-augmented Conversational Recommendation
Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items.
Conversational AI Systems for Social Good: Opportunities and Challenges
Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts.
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation
Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source.
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
Hydrophobic interaction determines docking affinity of SARS CoV 2 variants with antibodies
Preliminary epidemiologic, phylogenetic and clinical findings suggest that several novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have increased transmissibility and decreased efficacy of several existing vaccines.
Conversational Query Rewriting with Self-supervised Learning
Conversational Query Rewriting (CQR) aims to simplify the multi-turn dialogue modeling into a single-turn problem by explicitly rewriting the conversational query into a self-contained utterance.
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
Multimodal Sentence Summarization via Multimodal Selective Encoding
Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.
On the Faithfulness for E-commerce Product Summarization
In this work, we present a model to generate e-commerce product summaries.
Group Contextual Encoding for 3D Point Clouds
In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.
Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis
Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which makes it challenging when converting a paragraph of texts into natural and expressive speech.
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking
However, to solve the AES task, previous works utilize shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss, respectively.
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
The role of hydrophobic interactions in folding of $β$-sheets
Exploring the protein-folding problem has been a long-standing challenge in molecular biology.
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
We annotate a multimodal product attribute value dataset that contains 87, 194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task.
A hydrophobic-interaction-based mechanism trigger docking between the SARS CoV 2 spike and angiotensin-converting enzyme 2
The hydrophobic interaction between the SARS-CoV-2 S and ACE2 protein is found to be significantly greater than that between SARS-CoV S and ACE2.
Neural Kalman Filtering for Speech Enhancement
Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural network (DNN) based methods which are data-driven.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-Attention Guided Copy Mechanism for Abstractive Summarization
Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary.
Incremental Learning for End-to-End Automatic Speech Recognition
In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintaining the performance on its originally learned ones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Speaker Diarization with Lexical Information
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Graph Sequential Network for Reasoning over Sequences
We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER.
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images
In this paper, we propose a diabetic retinopathy generative adversarial network (DR-GAN) to synthesize high-resolution fundus images which can be manipulated with arbitrary grading and lesion information.
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
Human conversations are complicated and building a human-like dialogue agent is an extremely challenging task.
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications.
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE.
Ranked #18 on
Link Prediction
on FB15k-237
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
Relation Module for Non-Answerable Predictions on Reading Comprehension
We test the relation module on the SQuAD 2. 0 dataset using both the BiDAF and BERT models as baseline readers.
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence.
Relation Module for Non-answerable Prediction on Question Answering
In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e. g. the recently proposed SQuAD 2. 0 task).
Zero-shot Text-to-SQL Learning with Auxiliary Task
Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task.
Multiple instance learning with graph neural networks
In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance.
Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial Imagination
We present a novel real-time, collaborative, and interactive AI painting system, Mappa Mundi, for artistic Mind Map creation.
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling
The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input.
Ranked #21 on
Visual Storytelling
on VIST
From Knowledge Map to Mind Map: Artificial Imagination
Imagination is one of the most important factors which makes an artistic painting unique and impressive.
Object-driven Text-to-Image Synthesis via Adversarial Training
In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes.
Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification
This paper aims to improve the widely used deep speaker embedding x-vector model.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
Ranked #28 on
Link Prediction
on FB15k-237
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e. g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm.
Deep Reinforcement Learning for NLP
Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems.
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task.
Ranked #24 on
Visual Storytelling
on VIST
Discourse-Aware Neural Rewards for Coherent Text Generation
In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text.
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
By contrasting with human-written captions and image-mismatched captions, the caption generator effectively exploits the inherent characteristics of human languages, and generates more discriminative captions.
Deep Communicating Agents for Abstractive Summarization
We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization.
Ranked #31 on
Abstractive Text Summarization
on CNN / Daily Mail
(using extra training data)
Stacked Cross Attention for Image-Text Matching
Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable.
Ranked #4 on
Image Retrieval
on PhotoChat
Natural Language to Structured Query Generation via Meta-Learning
In conventional supervised training, a model is trained to fit all the training examples.
Ranked #7 on
Code Generation
on WikiSQL
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models.
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy
For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate.
From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots
Conversational systems have come a long way since their inception in the 1960s.
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality
Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of instantaneous input signal-to-noise ratios.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Ranked #1 on
Text-to-Image Generation
on MS-COCO
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets.
Ranked #2 on
Image Classification
on Food-101N
(using extra training data)
On the Discrimination-Generalization Tradeoff in GANs
When evaluated with neural distance, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set.
A Neural-Symbolic Approach to Design of CAPTCHA
To address this, this paper promotes image/visual captioning based CAPTCHAs, which is robust against machine-learning-based attacks.
Tensor Product Generation Networks for Deep NLP Modeling
We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks.
Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration
In this challenge, we propose a model-based vehicle localization method, which builds a kernel at each patch of the 3D deformable vehicle model and associates them with constraints in 3D space.
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge.
Ranked #30 on
Visual Question Answering (VQA)
on VQA v2 test-std
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning.
Ranked #29 on
Visual Question Answering (VQA)
on VQA v2 test-std
StyleNet: Generating Attractive Visual Captions With Styles
We propose a novel framework named StyleNet to address the task of generating attractive captions for images and videos with different styles.
Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet).
Adversarial Ranking for Language Generation
Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group.
Ranked #1 on
Text Generation
on Chinese Poems
Question-Answering with Grammatically-Interpretable Representations
In our application of TPRN, internal representations learned by end-to-end optimization in a deep neural network performing a textual question-answering (QA) task can be interpreted using basic concepts from linguistic theory.
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces.
Character-level Deep Conflation for Business Data Analytics
Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity.
Deep Learning with Low Precision by Half-wave Gaussian Quantization
The problem of quantizing the activations of a deep neural network is considered.
Learning Generic Sentence Representations Using Convolutional Neural Networks
We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes.
Semantic Compositional Networks for Visual Captioning
The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag.
Bi-directional Attention with Agreement for Dependency Parsing
We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions.
Ranked #4 on
Chinese Dependency Parsing
on Chinese Pennbank
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base.
Unsupervised Learning of Predictors from Unpaired Input-Output Samples
In particular, we show that with regularization via a generative model, learning with the proposed unsupervised objective function converges to an optimal solution.
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.
Visual Storytelling
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling.
A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories
We created a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation.
Character-Level Question Answering with Attention
We show that a character-level encoder-decoder framework can be successfully applied to question answering with a structured knowledge base.
Rich Image Captioning in the Wild
We present an image caption system that addresses new challenges of automatically describing images in the wild.
Generating Natural Questions About an Image
There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images.
Basic Reasoning with Tensor Product Representations
In this paper we present the initial development of a general theory for mapping inference in predicate logic to computation over Tensor Product Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)).
Reasoning in Vector Space: An Exploratory Study of Question Answering
Question answering tasks have shown remarkable progress with distributed vector representation.
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
Stacked Attention Networks for Image Question Answering
Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively.
Ranked #5 on
Visual Question Answering (VQA)
on VQA v1 test-std
Recurrent Reinforcement Learning: A Hybrid Approach
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states.
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model.
Language Models for Image Captioning: The Quirks and What Works
Two recent approaches have achieved state-of-the-art results in image captioning.
Joint Learning of Distributed Representations for Images and Texts
The learned representations attempt to capture the combination of various visual concepts and cues.
A Deep Embedding Model for Co-occurrence Learning
Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items.
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
The results show that the proposed method in this paper significantly outperforms it for web document retrieval task.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
We consider learning representations of entities and relations in KBs using the neural-embedding approach.
Ranked #10 on
Link Prediction
on UMLS
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Ranked #1 on
Image Captioning
on COCO Captions test
Learning Multi-Relational Semantics Using Neural-Embedding Models
In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework.
Learning Semantic Representations for the Phrase Translation Model
The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation sys-tem, leading to a gain of 0. 7-1. 0 BLEU points.
Learning deep structured semantic models for web search using clickthrough data
The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data.
