Search Results for author:
Found 143 papers, 70 papers with code
Continual Learning via Explicit Structure Learning
During structure learning, the model optimizes for the best structure for the current task.
The Thieves on Sesame Street are Polyglots - Extracting Multilingual Models from Monolingual APIs
Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim{'}s labels for that data.
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging
The concept of Dialogue Act (DA) is universal across different task-oriented dialogue domains - the act of {``}request{''} carries the same speaker intention whether it is for restaurant reservation or flight booking.
Large Language Models: A Survey
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022.
Converse: A Tree-Based Modular Task-Oriented Dialogue System
At the core of the struggle is the need to script every single turn of interactions between the bot and the human user.
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations.
Evaluating State-of-the-Art Classification Models Against Bayes Optimality
Moreover, we show that by varying the temperature of the learned flow models, we can generate synthetic datasets that closely resemble standard benchmark datasets, but with almost any desired Bayes error.
Neural Bayes: A Generic Parameterization Method for Unsupervised Learning
Disjoint Manifold Separation: Neural Bayes allows us to formulate an objective which can optimally label samples from disjoint manifolds present in the support of a continuous distribution.
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function.
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill.
Online Structured Meta-learning
When a new task is encountered, it constructs a meta-knowledge pathway by either utilizing the most relevant knowledge blocks or exploring new blocks.
Explaining and Improving Model Behavior with k Nearest Neighbor Representations
Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens.
Explaining Creative Artifacts
Human creativity is often described as the mental process of combining associative elements into a new form, but emerging computational creativity algorithms may not operate in this manner.
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start
We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data.
Ranked #8 on
Semantic Parsing
on spider
Composed Variational Natural Language Generation for Few-shot Intents
In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario.
GeDi: Generative Discriminator Guided Sequence Generation
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.
Central Yup'ik and Machine Translation of Low-Resource Polysynthetic Languages
Machine translation tools do not yet exist for the Yup'ik language, a polysynthetic language spoken by around 8, 000 people who live primarily in Southwest Alaska.
Photon: A Robust Cross-Domain Text-to-SQL System
Natural language interfaces to databases (NLIDB) democratize end user access to relational data.
SummEval: Re-evaluating Summarization Evaluation
The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.
DART: Open-Domain Structured Data Record to Text Generation
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions.
Theory-Inspired Path-Regularized Differential Network Architecture Search
Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during the search.
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Transformer architectures have proven to learn useful representations for protein classification and generation tasks.
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
When the trainable network is the quadratic Taylor model of a wide two-layer network, we show that neural representation can achieve improved sample complexities compared with the raw input: For learning a low-rank degree-$p$ polynomial ($p \geq 4$) in $d$ dimension, neural representation requires only $\tilde{O}(d^{\lceil p/2 \rceil})$ samples, while the best-known sample complexity upper bound for the raw input is $\tilde{O}(d^{p-1})$.
A High-Quality Multilingual Dataset for Structured Documentation Translation
We build and evaluate translation models for seven target languages from English, with several different copy mechanisms and an XML-constrained beam search.
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines.
WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications.
Ranked #5 on
Online Action Detection
on THUMOS'14
EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions.
It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations
Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e. g., African American Vernacular English, Colloquial Singapore English, etc.).
ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.
A Simple Language Model for Task-Oriented Dialogue
Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.
Ranked #2 on
Response Generation
on MMConv
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
In experiments conducted on MTurk, an AI tax policy provides an equality-productivity trade-off that is similar to that provided by the Saez framework along with higher inverse-income weighted social welfare.
TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue
The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice.
An investigation of phone-based subword units for end-to-end speech recognition
For Switchboard, our phone-based BPE system achieves 6. 8\%/14. 4\% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6. 3\%/13. 3\% WER.
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness.
Ranked #126 on
Domain Generalization
on PACS
ProGen: Language Modeling for Protein Generation
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
Towards Noise-resistant Object Detection with Noisy Annotations
We address the challenging problem of training object detectors with noisy annotations, where the noise contains a mixture of label noise and bounding box noise.
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
Disjoint Manifold Labeling: Neural Bayes allows us to formulate an objective which can optimally label samples from disjoint manifolds present in the support of a continuous distribution.
Tree-structured Attention with Hierarchical Accumulation
Incorporating hierarchical structures like constituency trees has been shown to be effective for various natural language processing (NLP) tasks.
Non-Autoregressive Dialog State Tracking
Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself.
Ranked #13 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data.
Ranked #4 on
Learning with noisy labels
on CIFAR-100N
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
We perform an extensive evaluation of skill discovery methods on controlled environments and show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width
We propose \emph{Taylorized training} as an initiative towards better understanding neural network training at finite width.
Limits of Detecting Text Generated by Large-Scale Language Models
Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns.
Learning from Noisy Anchors for One-stage Object Detection
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their intersection-over-union (IoU) with corresponding groundtruth objects.
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.
Ranked #26 on
Question Answering
on HotpotQA
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models
Our approach is model agnostic and can be easily applied on different future teacher model architectures.
ERASER: A Benchmark to Evaluate Rationalized NLP Models
We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i. e., the degree to which provided rationales influenced the corresponding predictions).
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
For instance, in tasks where the agent must achieve some goal state, simple distance-to-goal reward shaping often fails, as it renders learning vulnerable to local optima.
WSLLN:Weakly Supervised Natural Language Localization Networks
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.
BERT is Not an Interlingua and the Bias of Tokenization
Multilingual transfer learning can benefit both high- and low-resource languages, but the source of these improvements is not well understood.
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework
Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent.
Evaluating the Factual Consistency of Abstractive Text Summarization
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.
Global Capacity Measures for Deep ReLU Networks via Path Sampling
Classical results on the statistical complexity of linear models have commonly identified the norm of the weights $\|w\|$ as a fundamental capacity measure.
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Dialog state tracking (DST) is a core component in task-oriented dialog systems.
Ranked #4 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
dialog state tracking
Multi-domain Dialogue State Tracking
+1
Predicting with High Correlation Features
In this paper, we consider distribution shift as a shift in the distribution of input features during test time that exhibit low correlation with targets in the training set.
Entropy Penalty: Towards Generalization Beyond the IID Assumption
This allows deep networks trained with Entropy Penalty to generalize well even under distribution shift of spurious features.
DIME: AN INFORMATION-THEORETIC DIFFICULTY MEASURE FOR AI DATASETS
Empirically, we estimate this lower bound using a neural network to compute DIME.
Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization
In this work, we propose a guided adaptive credit assignment method to do effectively credit assignment for policy gradient methods.
Learning World Graph Decompositions To Accelerate Reinforcement Learning
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
Near-Zero-Cost Differentially Private Deep Learning with Teacher Ensembles
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
Ranked #8 on
Dialogue State Tracking
on CoSQL
CTRL: A Conditional Transformer Language Model for Controllable Generation
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
In this work, we propose a fully unsupervised model, Deleter, that is able to discover an "optimal deletion path" for an arbitrary sentence, where each intermediate sequence along the path is a coherent subsequence of the previous one.
Pretrained AI Models: Performativity, Mobility, and Change
The paradigm of pretrained deep learning models has recently emerged in artificial intelligence practice, allowing deployment in numerous societal settings with limited computational resources, but also embedding biases and enabling unintended negative uses.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
We focus on the cross-domain context-dependent text-to-SQL generation task.
Ranked #5 on
Text-To-SQL
on SParC
WSLLN: Weakly Supervised Natural Language Localization Networks
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.
Neural Text Summarization: A Critical Evaluation
Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document.
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Explain Yourself! Leveraging Language Models for Commonsense Reasoning
Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input.
Ranked #23 on
Common Sense Reasoning
on CommonsenseQA
SParC: Cross-Domain Semantic Parsing in Context
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
On the Generalization Gap in Reparameterizable Reinforcement Learning
For this problem class, estimating the expected return is efficient and the trajectory can be computed deterministically given peripheral random variables, which enables us to study reparametrizable RL using supervised learning and transfer learning theory.
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.
Cross-Lingual Natural Language Inference
Data Augmentation
+3
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Ranked #15 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
Unifying Question Answering, Text Classification, and Regression via Span Extraction
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different.
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code.
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks.
StartNet: Online Detection of Action Start in Untrimmed Videos
We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos.
Competitive Experience Replay
We propose a novel method called competitive experience replay, which efficiently supplements a sparse reward by placing learning in the context of an exploration competition between a pair of agents.
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge.
Ranked #4 on
Task-Oriented Dialogue Systems
on KVRET
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.
Ranked #115 on
Vision and Language Navigation
on VLN Challenge
Natural Language Visual Grounding
Vision and Language Navigation
+2
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document.
Ranked #5 on
Question Answering
on WikiHop
AdaFrame: Adaptive Frame Selection for Fast Video Recognition
We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition.
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation
In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA.
Correction Networks: Meta-Learning for Zero-Shot Learning
We propose a model that learns to perform zero-shot classification using a meta-learner that is trained to produce a correction to the output of a previously trained learner.
Identifying Generalization Properties in Neural Networks
In particular, we prove that model generalization ability is related to the Hessian, the higher-order "smoothness" terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters.
Multi-Hop Knowledge Graph Reasoning with Reward Shaping
Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs).
Improving Abstraction in Text Summarization
Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document.
Ranked #4 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Global-Locally Self-Attentive Encoder for Dialogue State Tracking
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Automatic Speech Recognition (ASR)
Dialogue State Tracking
+3
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
In low-resource supervised setting, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain.
The Natural Language Decathlon: Multitask Learning as Question Answering
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
Using Mode Connectivity for Loss Landscape Analysis
Mode connectivity is a recently introduced frame- work that empirically establishes the connected- ness of minima by finding a high accuracy curve between two independently trained models.
Efficient and Robust Question Answering from Minimal Context over Documents
Neural models for question answering (QA) over documents have achieved significant performance improvements.
Ranked #3 on
Question Answering
on NewsQA
Global-Locally Self-Attentive Dialogue State Tracker
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
End-to-End Dense Video Captioning with Masked Transformer
To address this problem, we propose an end-to-end transformer model for dense video captioning.
Ranked #12 on
Video Captioning
on YouCook2
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation
Domain adaptation plays an important role for speech recognition models, in particular, for domains that have low resources.
An Analysis of Neural Language Modeling at Multiple Scales
Many of the leading approaches in language modeling introduce novel, complex and specialized architectures.
Contextual Salience for Fast and Accurate Sentence Vectors
We introduce contextual salience (CoSal), a measure of word importance that uses the distribution of context vectors to normalize distances and weights.
Interpretable Counting for Visual Question Answering
Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA).
Improving Generalization Performance by Switching from Adam to SGD
Concretely, we propose SWATS, a simple strategy which switches from Adam to SGD when a triggering condition is satisfied.
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills.
A Flexible Approach to Automated RNN Architecture Generation
The process of designing neural architectures requires expert knowledge and extensive trial and error.
Block-diagonal Hessian-free Optimization for Training Neural Networks
Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence.
Improved Regularization Techniques for End-to-End Speech Recognition
We augment audio data through random perturbations of tempo, pitch, volume, temporal alignment, and adding random noise. We further investigate the effect of dropout when applied to the inputs of all layers of the network.
Improving End-to-End Speech Recognition with Policy Learning
However, there is usually a disparity between the negative maximum likelihood and the performance metric used in speech recognition, e. g., word error rate (WER).
Ranked #57 on
Speech Recognition
on LibriSpeech test-clean
Learning when to skim and when to read
Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset.
Non-Autoregressive Neural Machine Translation
Existing approaches to neural machine translation condition each output word on previously generated outputs.
Ranked #3 on
Machine Translation
on IWSLT2015 English-German
Weighted Transformer Network for Machine Translation
State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion.
Ranked #24 on
Machine Translation
on WMT2014 English-French
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate.
Ranked #28 on
Question Answering
on SQuAD1.1 dev
Towards Neural Machine Translation with Latent Tree Attention
Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
A significant amount of the world's knowledge is stored in relational databases.
Ranked #9 on
Code Generation
on WikiSQL
Regularizing and Optimizing LSTM Language Models
Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering.
Ranked #17 on
Language Modelling
on Penn Treebank (Word Level)
Revisiting Activation Regularization for Language RNNs
Both of these techniques require minimal modification to existing RNN architectures and result in performance improvements comparable or superior to more complicated regularization techniques or custom cell architectures.
Learned in Translation: Contextualized Word Vectors
For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art.
Ranked #9 on
Text Classification
on TREC-6
A Deep Reinforced Model for Abstractive Summarization
We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).
Ranked #6 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation.
A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
LSTMs have become a basic building block for many deep NLP models.
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks.
Ranked #3 on
Chunking
on Penn Treebank
Dynamic Coattention Networks For Question Answering
Several deep learning models have been proposed for question answering.
Ranked #2 on
Open-Domain Question Answering
on SQuAD1.1
Quasi-Recurrent Neural Networks
Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences.
Ranked #15 on
Machine Translation
on IWSLT2015 German-English
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
Recurrent neural networks have been very successful at predicting sequences of words in tasks such as language modeling.
Ranked #34 on
Language Modelling
on Penn Treebank (Word Level)
Pointer Sentinel Mixture Models
Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies.
Dynamic Memory Networks for Visual and Textual Question Answering
Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering.
Ranked #4 on
Visual Question Answering (VQA)
on VQA v1 test-std
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #67 on
Sentiment Analysis
on SST-2 Binary classification
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.
Ranked #1 on
Semantic Similarity
on SICK
Global Belief Recursive Neural Networks
Recursive Neural Networks have recently obtained state of the art performance on several natural language processing tasks.
GloVe: Global Vectors for Word Representation
Ranked #14 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
Grounded Compositional Semantics for Finding and Describing Images with Sentences
Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images.
Reasoning With Neural Tensor Networks for Knowledge Base Completion
We assess the model by considering the problem of predicting additional true relations between entities given a partial knowledge base.
Zero-Shot Learning Through Cross-Modal Transfer
This work introduces a model that can recognize objects in images even if no training data is available for the objects.
A Bayesian Analysis of Dynamics in Free Recall
We develop a probabilistic model of human memory performance in free recall experiments.
