Search Results for author:
Found 269 papers, 177 papers with code
Multi-Reward Reinforced Summarization with Saliency and Entailment
Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy.
Ranked #41 on
Abstractive Text Summarization
on CNN / Daily Mail
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation
An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document.
Ranked #33 on
Text Summarization
on GigaWord
Object Ordering with Bidirectional Matchings for Visual Reasoning
Visual reasoning with compositional natural language instructions, e. g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image.
Robust Machine Comprehension Models via Adversarial Training
It is shown that many published models for the Stanford Question Answering Dataset (Rajpurkar et al., 2016) lack robustness, suffering an over 50% decrease in F1 score during adversarial evaluation based on the AddSent (Jia and Liang, 2017) algorithm.
Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models
More importantly, we next interpret what these neural models have learned about the linguistic characteristics of AD patients, via analysis based on activation clustering and first-derivative saliency techniques.
Source-Target Inference Models for Spatial Instruction Understanding
Models that can execute natural language instructions for situated robotic tasks such as assembly and navigation have several useful applications in homes, offices, and remote scenarios.
Hierarchically-Attentive RNN for Album Summarization and Storytelling
For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story.
Ranked #15 on
Visual Storytelling
on VIST
(BLEU-3 metric)
Multi-Task Video Captioning with Video and Entailment Generation
Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data.
Reinforced Video Captioning with Entailment Rewards
Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training.
Video Highlight Prediction Using Audience Chat Reactions
Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis.
The Role of Context Types and Dimensionality in Learning Word Embeddings
We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks.
Contextual RNN-GANs for Abstract Reasoning Diagram Generation
Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence.
Coherent Dialogue with Attention-based Language Models
We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism.
Sort Story: Sorting Jumbled Images and Captions into Stories
Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication.
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning.
Interpreting Neural Networks to Improve Politeness Comprehension
We present an interpretable neural network approach to predicting and understanding politeness in natural language requests.
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions
We introduce the novel problem of determining the relevance of questions to images in VQA.
Who did What: A Large-Scale Person-Centered Cloze Dataset
We have constructed a new "Who-did-What" dataset of over 200, 000 fill-in-the-gap (cloze) multiple choice reading comprehension problems constructed from the LDC English Gigaword newswire corpus.
Charagram: Embedding Words and Sentences via Character n-grams
We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences.
Learning Articulated Motion Models from Visual and Lingual Signals
In this paper, we present a multimodal learning framework that incorporates both visual and lingual information to estimate the structure and parameters that define kinematic models of articulated objects.
Mapping Unseen Words to Task-Trained Embedding Spaces
We consider the supervised training setting in which we learn task-specific word embeddings.
We Are Humor Beings: Understanding and Predicting Visual Humor
We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level.
Towards Universal Paraphrastic Sentence Embeddings
We again find that the word averaging models perform well for sentence similarity and entailment, outperforming LSTMs.
Accurate Vision-based Vehicle Localization using Satellite Imagery
We propose a method for accurately localizing ground vehicles with the aid of satellite imagery.
From Paraphrase Database to Compositional Paraphrase Model and Back
The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates.
Web-scale Surface and Syntactic n-gram Features for Dependency Parsing
We develop novel first- and second-order features for dependency parsing based on the Google Syntactic Ngrams corpus, a collection of subtree counts of parsed sentences from scanned books.
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification
In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation.
Ranked #2 on
Text Simplification
on Newsela
Closed-Book Training to Improve Summarization Encoder Memory
A good neural sequence-to-sequence summarization model should have a strong encoder that can distill and memorize the important information from long input texts so that the decoder can generate salient summaries based on the encoder's memory.
Ranked #40 on
Abstractive Text Summarization
on CNN / Daily Mail
Incorporating Background Knowledge into Video Description Generation
We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents.
Towards Improving Abstractive Summarization via Entailment Generation
Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-to-sequence models.
What are You Talking About? Text-to-Image Coreference
In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing.
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods.
Good, Great, Excellent: Global Inference of Semantic Intensities
Adjectives like good, great, and excellent are similar in meaning, but differ in intensity.
Efficient Generation of Motion Plans from Attribute-Based Natural Language Instructions Using Dynamic Constraint Mapping
We generate a factor graph from natural language instructions called the Dynamic Grounding Graph (DGG), which takes latent parameters into account.
Robotics
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning
To address these issues, we present AutoSeM, a two-stage MTL pipeline, where the first stage automatically selects the most useful auxiliary tasks via a Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage learns the training mixing ratio of these selected auxiliary tasks via a Gaussian Process based Bayesian optimization framework.
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning
Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.
Improving Visual Question Answering by Referring to Generated Paragraph Captions
These paragraph captions can hence contain substantial information of the image for tasks such as visual question answering.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
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.
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.
Disentangling Online Chats with DAG-Structured LSTMs
In this paper, we apply DAG-LSTMs to the conversation disentanglement task.
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.
The Effect of Pretraining on Extractive Summarization for Scientific Documents
Large pretrained models have seen enormous success in extractive summarization tasks.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models
As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support.
Improving Vision-and-Language Navigation by Generating Future-View Image Semantics
We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step.
Visual Programming for Text-to-Image Generation and Evaluation
First, we introduce VPGen, an interpretable step-by-step T2I generation framework that decomposes T2I generation into three steps: object/count generation, layout generation, and image generation.
Exploring Continual Learning for Code Generation Models
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
On Conditional and Compositional Language Model Differentiable Prompting
Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks.
VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning
Our experiments demonstrate that VideoDirectorGPT framework substantially improves layout and movement control in both single- and multi-scene video generation and can generate multi-scene videos with visual consistency across scenes, while achieving competitive performance with SOTAs in open-domain single-scene T2V generation.
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
We first determine the sparsity ratios of different layers or blocks by leveraging the global importance score, which is efficiently computed based on the zeroth-order approximation of the global model gradients.
DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning
In the first stage, we use LLMs to generate and iteratively refine 'diagram plans' (in a planner-auditor feedback loop) which describe all the entities (objects and text labels), their relationships (arrows or lines), and their bounding box layouts.
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into account multiple aspects and criteria.
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
With extensive experimentation and human evaluation on a range of model configurations (LLM, VQA, and T2I), we empirically demonstrate that DSG addresses the challenges noted above.
ADaPT: As-Needed Decomposition and Planning with Language Models
Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment.
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation
We present CoDi-2, a versatile and interactive Multimodal Large Language Model (MLLM) that can follow complex multimodal interleaved instructions, conduct in-context learning (ICL), reason, chat, edit, etc., in an any-to-any input-output modality paradigm.
VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation
Outdoor Vision-and-Language Navigation (VLN) requires an agent to navigate through realistic 3D outdoor environments based on natural language instructions.
Rethinking Machine Unlearning for Large Language Models
We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning.
Evaluating Very Long-Term Conversational Memory of LLM Agents
Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 300 turns and 9K tokens on avg., over up to 35 sessions.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training
Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to attend more closely to these regions of interest.
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
In this paper, we introduce SELMA: Skill-Specific Expert Learning and Merging with Auto-Generated Data, a novel paradigm to improve the faithfulness of T2I models by fine-tuning models on automatically generated, multi-skill image-text datasets, with skill-specific expert learning and merging.
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents
Instead of directly employing LLMs as agents, can we use LLMs' reasoning capabilities to adaptively create training environments to help smaller embodied RL agents learn useful skills that they are weak at?
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
Ctrl-Adapter provides diverse capabilities including image control, video control, video control with sparse frames, multi-condition control, compatibility with different backbones, adaptation to unseen control conditions, and video editing.
Data Factors for Better Compositional Generalization
However, in contrast to this poor performance, state-of-the-art models trained on larger and more general datasets show better generalization ability.
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.
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.
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.
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.
HistAlign: Improving Context Dependency in Language Generation by Aligning with History
Cache-LMs, which augment LMs with a memory of recent history, can increase context dependency and have shown remarkable performance in diverse language generation tasks.
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.
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).
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.
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
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.
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.
Interactive Query-Assisted Summarization via Deep Reinforcement Learning
Interactive summarization is a task that facilitates user-guided exploration of information within a document set.
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.
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.
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.
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.
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.
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).
Exclusive Supermask Subnetwork Training for Continual Learning
Although there is no forgetting, the performance of SupSup is sub-optimal because fixed weights restrict its representational power.
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.
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.
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.
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
Debiasing Multimodal Models via Causal Information Minimization
In this paper, we study bias arising from confounders in a causal graph for multimodal data and examine a novel approach that leverages causally-motivated information minimization to learn the confounder representations.
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.
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.
Generating Summaries with Controllable Readability Levels
Readability refers to how easily a reader can understand a written text.
DAM: Dynamic Adapter Merging for Continual Video QA Learning
Our DAM model outperforms prior state-of-the-art continual learning approaches by 9. 1% while exhibiting 1. 9% less forgetting on 6 VidQA datasets spanning various domains.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents.
SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories
With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online.
Analyzing Compositionality-Sensitivity of NLI Models
Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i. e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models' actual compositionality awareness.
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.
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
Learning from multimodal data has become a popular research topic in recent years.
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.
Non-Sequential Graph Script Induction via Multimedia Grounding
To automate the induction of such graph scripts for given tasks, we propose to take advantage of loosely aligned videos of people performing the tasks.
Soft Self-Consistency Improves Language Model Agents
Current "sample and select" methods such as self-consistency (SC) rely on majority voting to score answers.
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.
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.
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.
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.
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.
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.
Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
Transformers generalize to novel compositions of structures and entities after being trained on a complex dataset, but easily overfit on datasets of insufficient complexity.
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Conducting a manual evaluation is considered an essential part of summary evaluation methodology.
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.
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.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
We introduce iFacetSum, a web application for exploring topical document sets.
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses.
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.
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.
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.
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.
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.
ReGAL: Refactoring Programs to Discover Generalizable Abstractions
While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the same functionality.
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i. e., the joint task of content selection and surface realization.
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models
We present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies that evaluate over-sensitivity to small and semantics-preserving edits, as well as Should-Change strategies that test if a model is over-stable against subtle yet semantics-changing modifications.
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?
ManyModalQA: Modality Disambiguation and QA over Diverse Inputs
By analyzing this model, we investigate which words in the question are indicative of the modality.
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies
Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE).
Merging by Matching Models in Task Parameter Subspaces
Model merging aims to cheaply combine individual task-specific models into a single multitask model.
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated.
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.
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.
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.
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
An increasing number of vision-language tasks can be handled with little to no training, i. e., in a zero and few-shot manner, by marrying large language models (LLMs) to vision encoders, resulting in large vision-language models (LVLMs).
Game-Based Video-Context Dialogue
Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers.
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).
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
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.
Cultural Vocal Bursts Intensity Prediction
Language Modelling
+5
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.
CREMA: Multimodal Compositional Video Reasoning via Efficient Modular Adaptation and Fusion
Furthermore, we propose a fusion module designed to compress multimodal queries, maintaining computational efficiency in the LLM while combining additional modalities.
Ranked #1 on
Question Answering
on SQA3D
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.
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning
There are two dominant approaches: (1) geometry-based data selection for maximizing data diversity in the coreset, and (2) functions that assign difficulty scores to samples based on training dynamics.
Multimodal Representation Learning by Alternating Unimodal Adaptation
This optimization process is controlled by a gradient modification mechanism to prevent the shared head from losing previously acquired information.
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.
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape.
Ranked #1 on
Layout-to-Image Generation
on LayoutBench
ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness
Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them.
Polite Dialogue Generation Without Parallel Data
We present three weakly-supervised models that can generate diverse polite (or rude) dialogue responses without parallel data.
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.
Evaluating the Factual Consistency of Large Language Models Through News 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.
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Despite the efficiency of PEFT methods, the size of expert models can make it onerous to retrieve expert models per query over high-latency networks like the Internet or serve multiple experts on a single GPU.
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension
Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context.
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.
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models
Experiments on seven widely-used commonsense and math reasoning benchmarks show that MAGDi improves the reasoning capabilities of smaller models, outperforming several methods that distill from a single teacher and multiple teachers.
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
We also show improvements in a transfer-only setup on the DUC-2004 dataset.
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.
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.
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).
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.
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?
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.
Ranked #19 on
Video Question Answering
on NExT-QA
(using extra training data)
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.
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.
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.
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
Experimentally, we show that even state-of-the-art model editing methods such as ROME struggle to truly delete factual information from models like GPT-J, as our whitebox and blackbox attacks can recover "deleted" information from an edited model 38% of the time.
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)
ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound
We introduce an audiovisual method for long-range text-to-video retrieval.
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.
Punny Captions: Witty Wordplay in Image Descriptions
Wit is a form of rich interaction that is often grounded in a specific situation (e. g., a comment in response to an event).
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
The speaker generates referring expressions, the listener comprehends referring expressions, and the reinforcer introduces a reward function to guide sampling of more discriminative expressions.
An Empirical Study of Multimodal Model Merging
In this paper, we expand on this concept to a multimodal setup by merging transformers trained on different modalities.
