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Found 86 papers, 36 papers with code
Contextualized Role Interaction for Neural Machine Translation
Word inputs tend to be represented as single continuous vectors in deep neural networks.
Talk The Walk: Navigating Grids in New York City through Grounded Dialogue
We introduce `"Talk The Walk", the first large-scale dialogue dataset grounded in action and perception.
Analyzing Dynamic Adversarial Training Data in the Limit
To create models that are robust across a wide range of test inputs, training datasets should include diverse examples that span numerous phenomena.
What's Hidden in a One-layer Randomly Weighted Transformer?
Hidden within a one-layer randomly weighted Transformer, we find that subnetworks that can achieve 29. 45/17. 29 BLEU on IWSLT14/WMT14.
Human-Adversarial Visual Question Answering
Human subjects interact with a state-of-the-art VQA model, and for each image in the dataset, attempt to find a question where the model's predicted answer is incorrect.
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions.
True Few-Shot Learning with Language Models
Here, we evaluate the few-shot ability of LMs when such held-out examples are unavailable, a setting we call true few-shot learning.
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform.
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
We further conduct a novel human-in-the-loop evaluation to show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8. 8% of the time on average, compared to 17. 6% for a model trained without synthetic data.
Cross-Modal Retrieval Augmentation for Multi-Modal Classification
Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing.
Retrieval Augmentation Reduces Hallucination in Conversation
Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2020).
Gradient-based Adversarial Attacks against Text Transformers
We propose the first general-purpose gradient-based attack against transformer models.
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little
A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i. e., discovering protocols that can generalize to independently trained agents.
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
We introduce a method to determine if a certain capability helps to achieve an accurate model of given data.
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
We provide a new dataset of ~40, 000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation.
Reservoir Transformers
We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated.
DynaSent: A Dynamic Benchmark for Sentiment Analysis
We introduce DynaSent ('Dynamic Sentiment'), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis.
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.
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels.
ANLIzing the Adversarial Natural Language Inference Dataset
We perform an in-depth error analysis of Adversarial NLI (ANLI), a recently introduced large-scale human-and-model-in-the-loop natural language inference dataset collected over multiple rounds.
Learning Optimal Representations with the Decodable Information Bottleneck
We address the question of characterizing and finding optimal representations for supervised learning.
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 #13 on
Question Answering
on HotpotQA
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Ranked #7 on
Question Answering
on Natural Questions (short)
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes.
Multi-Dimensional Gender Bias Classification
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
Unsupervised Question Decomposition for Question Answering
We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering.
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.
On the interaction between supervision and self-play in emergent communication
A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training.
Generating Interactive Worlds with Text
We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Models often easily learn biases present in the training data, and their predictions directly reflect this bias.
Seeded self-play for language learning
Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language.
Finding Generalizable Evidence by Learning to Convince Q\&A Models
We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed.
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.
Hyperbolic Graph Neural Networks
Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise.
Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.
Finding Generalizable Evidence by Learning to Convince Q&A Models
We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed.
Countering Language Drift via Visual Grounding
Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans.
Supervised Multimodal Bitransformers for Classifying Images and Text
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.
Why Build an Assistant in Minecraft?
In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.
Learning to Speak and Act in a Fantasy Text Adventure Game
We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.
What makes a good conversation? How controllable attributes affect human judgments
A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them.
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies.
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
No Training Required: Exploring Random Encoders for Sentence Classification
We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i. e., using nothing but random parameterizations.
Emergent Linguistic Phenomena in Multi-Agent Communication Games
In this work, we propose a computational framework in which agents equipped with communication capabilities simultaneously play a series of referential games, where agents are trained using deep reinforcement learning.
Jump to better conclusions: SCAN both left and right
Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models.
Talk the Walk: Navigating New York City through Grounded Dialogue
We introduce "Talk The Walk", the first large-scale dialogue dataset grounded in action and perception.
Code-Switched Named Entity Recognition with Embedding Attention
We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data.
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores.
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods.
Dynamic Meta-Embeddings for Improved Sentence Representations
While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves.
Ranked #48 on
Natural Language Inference
on SNLI
SentEval: An Evaluation Toolkit for Universal Sentence Representations
We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating.
Ranked #4 on
Dialogue Generation
on Persona-Chat
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment.
Emergent Translation in Multi-Agent Communication
While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans.
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding.
Learning Visually Grounded Sentence Representations
We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding.
Automatically Generating Rhythmic Verse with Neural Networks
We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms.
Emergent Communication in a Multi-Modal, Multi-Step Referential Game
Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration.
Poincaré Embeddings for Learning Hierarchical Representations
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.
Ranked #1 on
Lexical Entailment
on HyperLex
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
Ranked #1 on
Semantic Textual Similarity
on SentEval
Cross-Lingual Natural Language Inference
Semantic Textual Similarity
+2
Learning to Negate Adjectives with Bilinear Models
We learn a mapping that negates adjectives by predicting an adjective{'}s antonym in an arbitrary word embedding model.
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation
Recent work on evaluating representation learning architectures in NLP has established a need for evaluation protocols based on subconscious cognitive measures rather than manually tailored intrinsic similarity and relatedness tasks.
Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns
Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically.
Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings
The conventional solution for handling sparsely labelled data is extensive feature engineering.
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research
Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences.
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
We introduce HyperLex - a dataset and evaluation resource that quantifies the extent of of the semantic category membership, that is, type-of relation also known as hyponymy-hypernymy or lexical entailment (LE) relation between 2, 616 concept pairs.
