Search Results for author:
Found 210 papers, 138 papers with code
ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI
We examine the effect of domain-specific external knowledge variations on deep large scale language model performance.
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
Learning from multimodal data has become a popular research topic in recent years.
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning
Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.
Interactive Query-Assisted Summarization via Deep Reinforcement Learning
Interactive summarization is a task that facilitates user-guided exploration of information within a document set.
On Curriculum Learning for Commonsense Reasoning
Hence, we examine the effect of a human-like easy-to-difficult curriculum during finetuning of language models for commonsense reasoning tasks.
Multimodal Intent Discovery from Livestream Videos
We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models.
The Effect of Pretraining on Extractive Summarization for Scientific Documents
Large pretrained models have seen enormous success in extractive summarization tasks.
Continual Few-Shot Learning for Text Classification
In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples.
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks
Motivated by the failure of a Transformer model on the SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing a command into actions, we propose two auxiliary sequence prediction tasks as additional training supervision.
NDH-Full: Learning and Evaluating Navigational Agents on Full-Length Dialogue
In this paper, we explore the Navigation from Dialogue History (NDH) task, which is based on the Cooperative Vision-and-Dialogue Navigation (CVDN) dataset, and present a state-of-the-art model which is built upon Vision-Language transformers.
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization
Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence (or visual story).
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
An Overview of Uncertainty Calibration for Text Classification and the Role of Distillation
Many recalibration methods have been proposed in the literature for quantifying predictive uncertainty and calibrating model outputs, with varying degrees of complexity.
Faithfulness-Aware Decoding Strategies for Abstractive Summarization
We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization.
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
In this paper, we find that we can change how a fact is stored in a model by editing weights that are in a different location than where existing methods suggest that the fact is stored.
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation
We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning.
Vision Transformers are Parameter-Efficient Audio-Visual Learners
To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT.
Ranked #1 on
Audio-visual Question Answering
on MUSIC-AVQA
VindLU: A Recipe for Effective Video-and-Language Pretraining
Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA.
Unifying Vision, Text, and Layout for Universal Document Processing
UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation.
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models.
Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
We present Perceiver-VL, a vision-and-language framework that efficiently handles high-dimensional multimodal inputs such as long videos and text.
Evaluating the Factual Consistency of Large Language Models Through Summarization
To generate summaries that are factually inconsistent, we generate summaries from a suite of summarization models that we have manually annotated as factually inconsistent.
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
We observe that (1) GPT-3 explanations are as grammatical as human explanations regardless of the hardness of the test samples, (2) for easy examples, GPT-3 generates highly supportive explanations but human explanations are more generalizable, and (3) for hard examples, human explanations are significantly better than GPT-3 explanations both in terms of label-supportiveness and generalizability judgements.
Evaluating and Improving Factuality in Multimodal Abstractive Summarization
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modality and thus are not adequate for vision-and-language summarization.
Exclusive Supermask Subnetwork Training for Continual Learning
Furthermore, we propose a novel KNN-based Knowledge Transfer (KKT) module that dynamically initializes a new task's mask based on previous tasks for improving knowledge transfer.
TVLT: Textless Vision-Language Transformer
In this work, we present the Textless Vision-Language Transformer (TVLT), where homogeneous transformer blocks take raw visual and audio inputs for vision-and-language representation learning with minimal modality-specific design, and do not use text-specific modules such as tokenization or automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior.
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters.
Ranked #2 on
Story Continuation
on FlintstonesSV
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization
Though extractive summarization is less prone to the common unfaithfulness issues of abstractive summaries, does that mean extractive is equal to faithful?
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
While vision-and-language models perform well on tasks such as visual question answering, they struggle when it comes to basic human commonsense reasoning skills.
Ranked #1 on
Common Sense Reasoning
on WinoGAViL
CoSIm: Commonsense Reasoning for Counterfactual Scene Imagination
Next, a counterfactual imagined scene change (in textual form) is applied, and the model has to predict the new response to the initial question based on this scene change.
SETSum: Summarization and Visualization of Student Evaluations of Teaching
Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSum helps them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both).
CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations
Empirically, on the Room-Across-Room dataset, we show that our multilingual agent gets large improvements in all metrics over the strong baseline model when generalizing to unseen environments with the cross-lingual language representation and the environment-agnostic visual representation.
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?
MPoSM can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction.
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
In this paper, we show that model FI supervision can meaningfully improve VQA model accuracy as well as performance on several Right-for-the-Right-Reason (RRR) metrics by optimizing for four key model objectives: (1) accurate predictions given limited but sufficient information (Sufficiency); (2) max-entropy predictions given no important information (Uncertainty); (3) invariance of predictions to changes in unimportant features (Invariance); and (4) alignment between model FI explanations and human FI explanations (Plausibility).
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
LST saves 69% of the memory costs to fine-tune the whole network, while other methods only save 26% of that in similar parameter usages (hence, 2. 7x more memory savings).
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Revealing Single Frame Bias for Video-and-Language Learning
Training an effective video-and-language model intuitively requires multiple frames as model inputs.
Ranked #2 on
Video Retrieval
on SSv2-template retrieval
Fine-grained Image Captioning with CLIP Reward
Toward more descriptive and distinctive caption generation, we propose using CLIP, a multimodal encoder trained on huge image-text pairs from web, to calculate multimodal similarity and use it as a reward function.
Ranked #26 on
Image Captioning
on COCO Captions
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets
Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes.
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 2020) pre-training objective to create pseudo-summaries that are both important and factual; (2) We introduce three complementary components for fine-tuning.
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made.
Ranked #1 on
Few-Shot Text Classification
on RAFT
Efficient Few-Shot Fine-Tuning for Opinion Summarization
In the same vein, we pre-train the adapters in a query-based manner on customer reviews and then fine-tune them on annotated datasets.
How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?
Since a skewed data distribution is considered to be harmful, a sampling strategy is usually used to balance languages in the corpus.
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language
We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general.
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning
In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs.
ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound
We introduce an audiovisual method for long-range text-to-video retrieval.
EnvEdit: Environment Editing for Vision-and-Language Navigation
Training on these edit-augmented environments prevents the agent from overfitting to existing environments and helps generalize better to new, unseen environments.
Ranked #2 on
Vision and Language Navigation
on RxR
(using extra training data)
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
In this work, we introduce Gradient-free Instructional Prompt Search (GrIPS), a gradient-free, edit-based search approach for improving task instructions for large language models.
LoopITR: Combining Dual and Cross Encoder Architectures for Image-Text Retrieval
In this work, we propose LoopITR, which combines them in the same network for joint learning.
CAISE: Conversational Agent for Image Search and Editing
To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests.
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Models
In this work, we investigate the visual reasoning capabilities and social biases of different text-to-image models, covering both multimodal transformer language models and diffusion models.
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding
Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.
Proposition-Level Clustering for Multi-Document Summarization
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.
Analyzing the Limits of Self-Supervision in Handling Bias in Language
We define three broad classes of task descriptions for these tasks: statement, question, and completion, with numerous lexical variants within each class.
Learning and Analyzing Generation Order for Undirected Sequence Models
On examples with a maximum source and target length of 30 from De-En, WMT'16 English-Romanian, and WMT'21 English-Chinese translation tasks, our learned order outperforms all heuristic generation orders on four out of six tasks.
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
Our results demonstrate that training the adapter with the weight-sharing technique (4. 18% of total parameters for image-text tasks and 3. 39% for video-text tasks) can match the performance of fine-tuning the entire model.
MLP Architectures for Vision-and-Language Modeling: An Empirical Study
Based on this, we ask an even bolder question: can we have an all-MLP architecture for VL modeling, where both VL fusion and the vision encoder are replaced with MLPs?
Detecting Moments and Highlights in Videos via Natural Language Queries
Each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w. r. t.
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
In this paper, we discuss approaches to detecting when models have beliefs about the world, and we improve on methods for updating model beliefs to be more truthful, with a focus on methods based on learned optimizers or hypernetworks.
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
Current approaches try to optimize for the cost incurred by users when adopting a recourse, but they assume that all users share the same cost function.
Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization
Prior work in this domain has shown that there is ample room for improvement in the generated image sequence in terms of visual quality, consistency and relevance.
Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks
Motivated by the failure of a Transformer model on the SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing a command into actions, we propose two auxiliary sequence prediction tasks that track the progress of function and argument semantics, as additional training supervision.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
We introduce iFacetSum, a web application for exploring topical document sets.
Finding a Balanced Degree of Automation for Summary Evaluation
In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid human evaluation method.
Continuous Language Generative Flow
Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial network (GAN) based models.
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
To defend against machine-generated fake news, an effective mechanism is urgently needed.
MTVR: Multilingual Moment Retrieval in Videos
We introduce mTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21. 8K TV show video clips.
EmailSum: Abstractive Email Thread Summarization
Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task.
Ranked #1 on
Email Thread Summarization
on EmailSum (short)
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback
The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment.
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
Each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w. r. t.
Ranked #1 on
Moment Retrieval
on Charades-STA
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world.
Ranked #4 on
Vision and Language Navigation
on RxR
(using extra training data)
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset.
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
Unlike language, where the text tokens are more independent, neighboring video tokens typically have strong correlations (e. g., consecutive video frames usually look very similar), and hence uniformly masking individual tokens will make the task too trivial to learn useful representations.
Ranked #5 on
Action Recognition
on Diving-48
Disentangling Online Chats with DAG-Structured LSTMs
In this paper, we apply DAG-LSTMs to the conversation disentanglement task.
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP
NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets.
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.
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph.
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and improved syntactic interpretability in the TPR layer outputs.
DeCEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy Minimization
Second, to alleviate the temporal misalignment issue, our method incorporates an entropy minimization-based constrained attention loss, to encourage the model to automatically focus on the correct caption from a pool of candidate ASR captions.
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
We also show improvements in a transfer-only setup on the DUC-2004 dataset.
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
In this paper, we study several under-explored dimensions of FI explanations, providing conceptual and empirical improvements for this form of explanation.
Extending Multi-Document Summarization Evaluation to the Interactive Setting
In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session.
Improving Generation and Evaluation of Visual Stories via Semantic Consistency
Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence/quality of generated characters, the relevance to captions, and the diversity of the generated images.
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks
For knowledge integration to yield peak performance, it is critical to select a knowledge graph (KG) that is well-aligned with the given task's objective.
Hidden Biases in Unreliable News Detection Datasets
Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task.
Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information
One key challenge in this task is to ground instructions with the current visual information that the agent perceives.
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning
We introduce distributed NLI, a new NLU task with a goal to predict the distribution of human judgements for natural language inference.
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
FixMyPose: Pose Correctional Captioning and Retrieval
During the correctional-captioning task, models must generate descriptions of how to move from the current to target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and correctional description.
Improving and Simplifying Pattern Exploiting Training
However, PET uses task-specific unlabeled data.
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.
Dual Reinforcement-Based Specification Generation for Image De-Rendering
Since these are sequence models, we must choose an ordering of the objects in the graphics programs for likelihood training.
Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that ClipBERT outperforms (or is on par with) existing methods that exploit full-length videos, suggesting that end-to-end learning with just a few sparsely sampled clips is often more accurate than using densely extracted offline features from full-length videos, proving the proverbial less-is-more principle.
Ranked #17 on
Visual Question Answering (VQA)
on MSRVTT-QA
(using extra training data)
Unifying Vision-and-Language Tasks via Text Generation
On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models.
Ranked #3 on
Image Captioning
on nocaps val
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data
In order to carefully control important properties of the data and explanations, we introduce a synthetic dataset for experiments, and we also make use of three existing datasets with explanations: e-SNLI, TACRED, and SemEval.
Robustness Gym: Unifying the NLP Evaluation Landscape
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems.
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
With the availability of the fast influence functions, we demonstrate their usefulness in four applications.
To what extent do human explanations of model behavior align with actual model behavior?
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior.
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling
To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues.
DORB: Dynamically Optimizing Multiple Rewards with Bandits
Further, it is important to consider using a dynamic combination and curriculum of metric rewards that flexibly changes over time.
ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments
During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural language instructions in a complex, realistic outdoor environment, but then also ARRAnge the collected objects part-by-part in an egocentric grid-layout environment.
HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification
We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and fact verification.
FENAS: Flexible and Expressive Neural Architecture Search
Architecture search is the automatic process of designing the model or cell structure that is optimal for the given dataset or task.
ConjNLI: Natural Language Inference Over Conjunctive Sentences
Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic.
What is More Likely to Happen Next? Video-and-Language Future Event Prediction
Given a video with aligned dialogue, people can often infer what is more likely to happen next.
Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision
We find that the main reason hindering this exploration is the large divergence in magnitude and distributions between the visually-grounded language datasets and pure-language corpora.
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English.
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
We provide code for the experiments in this paper at https://github. com/peterbhase/LAS-NL-Explanations
What Can We Learn from Collective Human Opinions on Natural Language Inference Data?
Analysis reveals that: (1) high human disagreement exists in a noticeable amount of examples in these datasets; (2) the state-of-the-art models lack the ability to recover the distribution over human labels; (3) models achieve near-perfect accuracy on the subset of data with a high level of human agreement, whereas they can barely beat a random guess on the data with low levels of human agreement, which compose most of the common errors made by state-of-the-art models on the evaluation sets.
PRover: Proof Generation for Interpretable Reasoning over Rules
First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation).
Evaluating Interactive Summarization: an Expansion-Based Framework
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results.
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection.
Simple Compounded-Label Training for Fact Extraction and Verification
Automatic fact checking is an important task motivated by the need for detecting and preventing the spread of misinformation across the web.
Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA
Moreover, our model is also comprised of dual-level attention (word/object and frame level), multi-head self/cross-integration for different sources (video and dense captions), and gates which pass more relevant information to the classifier.
MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Generating multi-sentence descriptions for videos is one of the most challenging captioning tasks due to its high requirements for not only visual relevance but also discourse-based coherence across the sentences in the paragraph.
Ranked #5 on
Video Captioning
on ActivityNet Captions
Towards Robustifying NLI Models Against Lexical Dataset Biases
While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting several dataset biases, and without deep understanding of the language semantics.
Diagnosing the Environment Bias in Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations.
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Through two kinds of simulation tests involving text and tabular data, we evaluate five explanations methods: (1) LIME, (2) Anchor, (3) Decision Boundary, (4) a Prototype model, and (5) a Composite approach that combines explanations from each method.
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions
For the first question, we conduct a thorough empirical study over analysis sets and find that in addition to the unstable final performance, the instability exists all along the training curve.
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension
In this work, we present several effective adversaries and automated data augmentation policy search methods with the goal of making reading comprehension models more robust to adversarial evaluation, but also improving generalization to the source domain as well as new domains and languages.
TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
The queries are also labeled with query types that indicate whether each of them is more related to video or subtitle or both, allowing for in-depth analysis of the dataset and the methods that built on top of it.
Ranked #2 on
Video Retrieval
on TVR
ManyModalQA: Modality Disambiguation and QA over Diverse Inputs
By analyzing this model, we investigate which words in the question are indicative of the modality.
Modality-Balanced Models for Visual Dialogue
The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue.
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses
In our work, we build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering.
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Next, we develop a DistanceNet model which uses these distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.
Adversarial NLI: A New Benchmark for Natural Language Understanding
We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.
Automatically Learning Data Augmentation Policies for Dialogue Tasks
Automatic data augmentation (AutoAugment) (Cubuk et al., 2019) searches for optimal perturbation policies via a controller trained using performance rewards of a sampled policy on the target task, hence reducing data-level model bias.
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task.
Ranked #48 on
Question Answering
on HotpotQA
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering
Second, since the traditional evaluation metrics (e. g., BLEU) often fall short in evaluating the quality of generated questions, we propose a QA-based evaluation method which measures the QG model's ability to mimic human annotators in generating QA training data.
Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning
Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question.
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder.
Ranked #1 on
Visual Question Answering (VQA)
on VizWiz 2018
Expressing Visual Relationships via Language
To push forward the research in this direction, we first introduce a new language-guided image editing dataset that contains a large number of real image pairs with corresponding editing instructions.
Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA
After adversarial training, the baseline's performance improves but is still limited on the adversarial evaluation.