Search Results for author:
Found 206 papers, 100 papers with code
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler
In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.
Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization
Neural text summarization has shown great potential in recent years.
Counterfactual Off-Policy Training for Neural Dialogue Generation
Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks.
VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View
In this work, we propose VELMA, an embodied LLM agent that uses a verbalization of the trajectory and of visual environment observations as contextual prompt for the next action.
Multilingual Conceptual Coverage in Text-to-Image Models
We propose "Conceptual Coverage Across Languages" (CoCo-CroLa), a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts.
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
When combined with a downstream image generation model, LayoutGPT outperforms text-to-image models/systems by 20-40% and achieves comparable performance as human users in designing visual layouts for numerical and spatial correctness.
ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers
To address these challenges, we propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the creation and verify their correctness.
Text-guided 3D Human Generation from 2D Collections
To address this \texttt{T3H} task, we propose Compositional Cross-modal Human (CCH).
Ranked #1 on
Text-to-3D-Human Generation
on SHHQ
EDIS: Entity-Driven Image Search over Multimodal Web Content
Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion.
Let's Think Frame by Frame: Evaluating Video Chain of Thought with Video Infilling and Prediction
We benchmark various vision-language models on VIP, demonstrating the potential to use vision-language models and LLMs to enhance video chain of thought reasoning.
On the Risk of Misinformation Pollution with Large Language Models
In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems.
Mitigating Language Model Hallucination with Interactive Question-Knowledge Alignment
To address this issue, we introduce MixAlign, a framework that interacts with both the user and the knowledge base to obtain and integrate clarifications on how the user question relates to the stored information.
INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback
In particular, since the advent of neural metrics, like COMET, BLEURT, and SEScore2, the newest generation of metrics show a high correlation with human judgment.
Fact-Checking Complex Claims with Program-Guided Reasoning
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
We also introduce a self-refinement stage, which utilizes the symbolic solver's error messages to revise symbolic formalizations.
LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor correlation with human judgments.
Discriminative Diffusion Models as Few-shot Vision and Language Learners
Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation.
Data Augmentation for Diverse Voice Conversion in Noisy Environments
Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on.
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation
We conduct a series of experiments to compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process.
Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings
We apply VCoT to the Visual Storytelling and WikiHow summarization datasets and demonstrate through human evaluation that VCoT offers novel and consistent synthetic data augmentation beating chain of thought baselines, which can be used to enhance downstream performance.
Multimodal Procedural Planning via Dual Text-Image Prompting
The key challenges of MPP are to ensure the informativeness, temporal coherence, and accuracy of plans across modalities.
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
We release Multimodal C4, an augmentation of the popular text-only C4 corpus with images interleaved.
Users are the North Star for AI Transparency
We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals.
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Diffusion models show promising generation capability for a variety of data.
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
In this work, we focus on Few-shot Learning with Auxiliary Data (FLAD), a training paradigm that assumes access to auxiliary data during few-shot learning in hopes of improving generalization.
Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning
In this study, we aim to examine the in-context learning phenomenon through a Bayesian lens, viewing large language models as topic models that implicitly infer task-related information from demonstrations.
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
CausalDialogue: Modeling Utterance-level Causality in Conversations
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
We show that, with consistent tokenization, the model performs better in both in-domain and out-of-domain datasets, with a notable average of +1. 7 F2 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets.
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI
Users' physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can lead to serious injury.
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
In this paper, we propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation.
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
In this work, we improve the compositional skills of T2I models, specifically more accurate attribute binding and better image compositions.
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning.
Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation
Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction.
Ranked #3 on
Video Prediction
on BAIR Robot Pushing
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search.
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding
Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency.
WikiWhy: Answering and Explaining Cause-and-Effect Questions
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging.
CPL: Counterfactual Prompt Learning for Vision and Language Models
Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP.
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe.
ULN: Towards Underspecified Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task to guide an embodied agent moving to a target position using language instructions.
Mitigating Covertly Unsafe Text within Natural Language Systems
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences.
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
However, the application of its text encoder solely for text understanding has been less explored.
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric?
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching.
Ranked #1 on
Question Answering
on ConvFinQA
Atomized Deep Learning Models
Deep learning models often tackle the intra-sample structure, such as the order of words in a sentence and pixels in an image, but have not pay much attention to the inter-sample relationship.
Knowledge-Grounded Reinforcement Learning
We introduce the concept, Knowledge-Grounded RL (KGRL), with a formal definition that an agent learns to follow external guidelines and develop its own policy.
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context.
Anticipating the Unseen Discrepancy for Vision and Language Navigation
In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency.
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
Masked visual modeling (MVM) has been recently proven effective for visual pre-training.
Ranked #1 on
Video Question Answering
on LSMDC-MC
Causal Balancing for Domain Generalization
While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations.
Neuro-Symbolic Procedural Planning with Commonsense Prompting
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps.
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation
Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
KETOD: Knowledge-Enriched Task-Oriented Dialogue
We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities.
Imagination-Augmented Natural Language Understanding
Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations.
End-to-end Dense Video Captioning as Sequence Generation
Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event.
Ranked #3 on
Dense Video Captioning
on ViTT
(CIDEr metric, using extra
training data)
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems
Our proposed method allows a single transformer model to directly walk on a large-scale knowledge graph to generate responses.
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains.
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers
Building natural language inference (NLI) benchmarks that are both challenging for modern techniques, and free from shortcut biases is difficult.
Relational Graph Learning for Grounded Video Description Generation
Such a setting can help explain the decisions of captioning models and prevents the model from hallucinating object words in its description.
VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
Further, unlike previous studies that found pre-training tasks on video inputs (e. g., masked frame modeling) not very effective, we design a new pre-training task, Masked Visual-token Modeling (MVM), for better video modeling.
MIC: Model-agnostic Integrated Cross-channel Recommenders
However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration.
Attacking Open-domain Question Answering by Injecting Misinformation
We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems.
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
In this work, we present such a system for the emergent domain of COVID-19.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space
We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus.
D-REX: Dialogue Relation Extraction with Explanations
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.
Ranked #7 on
Dialog Relation Extraction
on DialogRE
FinQA: A Dataset of Numerical Reasoning over Financial Data
In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.
Ranked #3 on
Question Answering
on FinQA
Neural Stylistic Response Generation with Disentangled Latent Variables
Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style.
Local Explanation of Dialogue Response Generation
To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation
Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references.
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task.
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
Although deep learning models have driven state-of-the-art performance on a wide array of tasks, they are prone to spurious correlations that should not be learned as predictive clues.
Language-Driven Image Style Transfer
We propose contrastive language visual artist (CLVA) that learns to extract visual semantics from style instructions and accomplish LDAST by the patch-wise style discriminator.
Semi-Supervised Policy Initialization for Playing Games with Language Hints
Using natural language as a hint can supply an additional reward for playing sparse-reward games.
Zero-shot Fact Verification by Claim Generation
However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive.
FoveaTer: Foveated Transformer for Image Classification
We propose Foveated Transformer (FoveaTer) model, which uses pooling regions and eye movements to perform object classification tasks using a Vision Transformer architecture.
Comparing Visual Reasoning in Humans and AI
Results show that the machine/human agreement scene descriptions are much lower than human/human agreement for our complex scenes.
Gaze Perception in Humans and CNN-Based Model
Making accurate inferences about other individuals' locus of attention is essential for human social interactions and will be important for AI to effectively interact with humans.
M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformers
LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video; 2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction.
Diagnosing Vision-and-Language Navigation: What Really Matters
Results show that indoor navigation agents refer to both object and direction tokens when making decisions.
On Hallucination and Predictive Uncertainty in Conditional Language Generation
Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent.
They, Them, Theirs: Rewriting with Gender-Neutral English
Responsible development of technology involves applications being inclusive of the diverse set of users they hope to support.
L2C: Describing Visual Differences Needs Semantic Understanding of Individuals
Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs.
DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents
Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner.
Investigating Memorization of Conspiracy Theories in Text Generation
In this work, we investigate the capability of language models to generate conspiracy theory text.
Modeling Disclosive Transparency in NLP Application Descriptions
Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable.
Unsupervised Multi-hop Question Answering by Question Generation
Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive.
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.
Investigating African-American Vernacular English in Transformer-Based Text Generation
The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts.
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
We propose a knowledge-grounded pre-training (KGPT), which consists of two parts, 1) a general knowledge-grounded generation model to generate knowledge-enriched text.
Ranked #9 on
KG-to-Text Generation
on WebNLG 2.0 (Unconstrained)
Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation
Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in real-world environments.
Ranked #3 on
Vision and Language Navigation
on Touchdown Dataset
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Ranked #16 on
Question Answering
on HotpotQA
SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning
In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity.
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates a real-life urban environment.
Ranked #4 on
Vision and Language Navigation
on Touchdown Dataset
(using extra training data)
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment
The spread of COVID-19 has become a significant and troubling aspect of society in 2020.
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
Classification
Cultural Vocal Bursts Intensity Prediction
+2
Knowledge Injection into Dialogue Generation via Language Models
First, we train a large-scale language model and query it as textual knowledge.
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations.
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering
In this work, we propose a simple and resource-efficient method to pretrain the paragraph encoder.
Counterfactual Off-Policy Training for Neural Response Generation
Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.
Evaluating Transformer-Based Multilingual Text Classification
As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures.
Logical Natural Language Generation from Open-Domain Tables
To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w. r. t.\ logical inference.
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching.
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing
We introduce HULK, a multi-task energy efficiency benchmarking platform for responsible natural language processing.
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
Disentangled Representation Learning with Wasserstein Total Correlation
However, Kullback-Leibler (KL) divergence-based total correlation is metric-agnostic and sensitive to data samples.
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
Models trained with our new objective yield significant improvements on the fact completion task.
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e. g., television) using only visual observations.
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling
In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
Classification
Cultural Vocal Bursts Intensity Prediction
+2
Table-to-Text Natural Language Generation with Unseen Schemas
In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training.
Towards Understanding Gender Bias in Relation Extraction
We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE.
Cross-Lingual Vision-Language Navigation
Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics.
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i. e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.
Neural Correction Model for Open-Domain Named Entity Recognition
In this work, to address the low precision and recall problems, we first utilize DBpedia as the source of distant supervision to annotate abstracts from Wikipedia and design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false entity labels.
A Benchmark Dataset for Learning to Intervene in Online Hate Speech
In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Many tasks in natural language processing can be viewed as multi-label classification problems.
Neural Gaussian Copula for Variational Autoencoder
We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models.
TabFact: A Large-scale Dataset for Table-based Fact Verification
To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.
Ranked #9 on
Table-based Fact Verification
on TabFact
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues.
Text Modeling with Syntax-Aware Variational Autoencoders
We propose syntax-aware variational autoencoders (SAVAEs) that dedicate a subspace in the latent dimensions dubbed syntactic latent to represent syntactic structures of sentences.
Multi-Task Self-Supervised Learning for Disfluency Detection
The pre-trained network is then fine-tuned using human-annotated disfluency detection training data.
Meta Reasoning over Knowledge Graphs
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.
What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog
In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.
TWEETQA: A Social Media Focused Question Answering Dataset
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.
What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.
In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.
Self-Supervised Dialogue Learning
The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations.
Mitigating Gender Bias in Natural Language Processing: Literature Review
In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP.
Self-Supervised Learning for Contextualized Extractive Summarization
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.
Deep Adversarial Learning for NLP
Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently.
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on
Data-to-Text Generation
on MULTIWOZ 2.1
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.
REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments
One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language.
Few-Shot NLG with Pre-Trained Language Model
Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs.
Learning to Decipher Hate Symbols
Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior.
Sentence Embedding Alignment for Lifelong Relation Extraction
We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks.
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.
Ranked #3 on
Entity Typing
on Ontonotes v5 (English)
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.
Ranked #2 on
Vision-Language Navigation
on Room2Room
Quantifying Uncertainties in Natural Language Processing Tasks
Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems.
Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning
Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime.
A Survey on Natural Language Processing for Fake News Detection
We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.
Towards Explainable NLP: A Generative Explanation Framework for Text Classification
Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions.
Implicit Regularization of Stochastic Gradient Descent in Natural Language Processing: Observations and Implications
To bridge this gap, we study the role of SGD implicit regularization in deep learning systems.
DOLORES: Deep Contextualized Knowledge Graph Embeddings
We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations.
Dirichlet Variational Autoencoder for Text Modeling
We introduce an improved variational autoencoder (VAE) for text modeling with topic information explicitly modeled as a Dirichlet latent variable.
WikiHow: A Large Scale Text Summarization Dataset
Sequence-to-sequence models have recently gained the state of the art performance in summarization.
Ranked #3 on
Text Summarization
on WikiHow
Analyzing and Interpreting Convolutional Neural Networks in NLP
Convolutional neural networks have been successfully applied to various NLP tasks.