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Found 43 papers, 23 papers with code
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
Hence, in order to achieve higher performance on our tasks, models need to effectively utilize such functional knowledge to infer the outcomes of actions, rather than relying solely on memorizing facts.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
Humans can abstract prior knowledge from very little data and use it to boost skill learning.
Deriving Commonsense Inference Tasks from Interactive Fictions
Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an indispensable cornerstone in building general AI systems.
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning
Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques.
Frustratingly Hard Evidence Retrieval for QA Over Books
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i. e., the question-answer pairs.
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes.
Context-Aware Conversation Thread Detection in Multi-Party Chat
In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.
Do Multi-hop Readers Dream of Reasoning Chains?
General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i. e. the ability to reason with information collected from multiple passages to derive the answer.
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging.
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i. e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.
Meta Reasoning over Knowledge Graphs
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
ACM-CNN consists of three types of modules: a coarse network, a fine network, and a smooth network.
TWEETQA: A Social Media Focused Question Answering Dataset
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.
Self-Supervised Learning for Contextualized Extractive Summarization
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval.
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes.
Hybrid Reinforcement Learning with Expert State Sequences
The empirical results show that (1) the agents are able to leverage state expert sequences to learn faster than pure reinforcement learning baselines, (2) our tensor-based action inference model is advantageous compared to standard deep neural networks in inferring expert actions, and (3) the hybrid policy optimization objective is robust against noise in expert state sequences.
Sentence Embedding Alignment for Lifelong Relation Extraction
We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks.
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.
Ranked #3 on
Entity Typing
on Ontonotes v5 (English)
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph.
Ranked #19 on
Relation Extraction
on SemEval-2010 Task 8
Few-shot Learning with Meta Metric Learners
Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners.
One-Shot Relational Learning for Knowledge Graphs
Knowledge graphs (KGs) are the key components of various natural language processing applications.
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains.
Dialog-based Interactive Image Retrieval
Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.
NE-Table: A Neural key-value table for Named Entities
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.
A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities
Many goal-oriented dialog tasks, especially ones in which the dialog system has to interact with external knowledge sources such as databases, have to handle a large number of Named Entities (NEs).
Faster Reinforcement Learning with Expert State Sequences
In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available.
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer.
Ranked #1 on
Open-Domain Question Answering
on Quasar
Eigenoption Discovery through the Deep Successor Representation
Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning.
Dilated Recurrent Neural Networks
To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.
Ranked #24 on
Sequential Image Classification
on Sequential MNIST
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.
Ranked #4 on
Open-Domain Question Answering
on Quasar
Robust Task Clustering for Deep Many-Task Learning
We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.
Deep Memory Networks for Attitude Identification
In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets.
DeepCas: an End-to-end Predictor of Information Cascades
While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features.
DeepGraph: Graph Structure Predicts Network Growth
Conventionally, a graph structure is represented using an adjacency matrix or a set of hand-crafted structural features.
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games
We present an adaptation of PGRD (policy-gradient for reward-design) for learning a reward-bonus function to improve UCT (a MCTS algorithm).
Action-Conditional Video Prediction using Deep Networks in Atari Games
Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection.
Reward Mapping for Transfer in Long-Lived Agents
We demonstrate that our approach can substantially improve the agent's performance relative to other approaches, including an approach that transfers policies.
