62 research outputs found
Language Understanding for Text-based Games Using Deep Reinforcement Learning
In this paper, we consider the task of learning control policies for
text-based games. In these games, all interactions in the virtual world are
through text and the underlying state is not observed. The resulting language
barrier makes such environments challenging for automatic game players. We
employ a deep reinforcement learning framework to jointly learn state
representations and action policies using game rewards as feedback. This
framework enables us to map text descriptions into vector representations that
capture the semantics of the game states. We evaluate our approach on two game
worlds, comparing against baselines using bag-of-words and bag-of-bigrams for
state representations. Our algorithm outperforms the baselines on both worlds
demonstrating the importance of learning expressive representations.Comment: 11 pages, Appearing at EMNLP, 201
Grounding Language for Transfer in Deep Reinforcement Learning
In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of environments provide a compact intermediate channel to
facilitate effective policy transfer. Specifically, by learning to ground the
meaning of text to the dynamics of the environment such as transitions and
rewards, an autonomous agent can effectively bootstrap policy learning on a new
domain given its description. We employ a model-based RL approach consisting of
a differentiable planning module, a model-free component and a factorized state
representation to effectively use entity descriptions. Our model outperforms
prior work on both transfer and multi-task scenarios in a variety of different
environments. For instance, we achieve up to 14% and 11.5% absolute improvement
over previously existing models in terms of average and initial rewards,
respectively.Comment: JAIR 201
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, extraction from new sources and reconciliation of extracted values,
which are repeated until sufficient evidence is collected. We approach the
problem using a reinforcement learning framework where our model learns to
select optimal actions based on contextual information. We employ a deep
Q-network, trained to optimize a reward function that reflects extraction
accuracy while penalizing extra effort. Our experiments on two databases -- of
shooting incidents, and food adulteration cases -- demonstrate that our system
significantly outperforms traditional extractors and a competitive
meta-classifier baseline.Comment: Appearing in EMNLP 2016 (12 pages incl. supplementary material
Characterisation of the effects and mechanism of action of rapamycin and genistein on acute myeloid leukemia using high-throughput techniques
Ph.DDOCTOR OF PHILOSOPH
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
Extreme classification (XC) involves predicting over large numbers of classes
(thousands to millions), with real-world applications like news article
classification and e-commerce product tagging. The zero-shot version of this
task requires generalization to novel classes without additional supervision.
In this paper, we develop SemSup-XC, a model that achieves state-of-the-art
zero-shot and few-shot performance on three XC datasets derived from legal,
e-commerce, and Wikipedia data. To develop SemSup-XC, we use automatically
collected semantic class descriptions to represent classes and facilitate
generalization through a novel hybrid matching module that matches input
instances to class descriptions using a combination of semantic and lexical
similarity. Trained with contrastive learning, SemSup-XC significantly
outperforms baselines and establishes state-of-the-art performance on all three
datasets considered, gaining up to 12 precision points on zero-shot and more
than 10 precision points on one-shot tests, with similar gains for recall@10.
Our ablation studies highlight the relative importance of our hybrid matching
module and automatically collected class descriptions.Comment: Published at ICML 2023. V2: camera ready version at ICML 202
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