An Overview of Natural Language State Representation for Reinforcement Learning

• URL: http://arxiv.org/abs/2007.09774v1
• Date: Sun, 19 Jul 2020 20:15:55 GMT
• Title: An Overview of Natural Language State Representation for Reinforcement Learning
• Authors: Brielen Madureira and David Schlangen
• Abstract summary: A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. This survey outlines the strategies used in the literature to build natural language state representations.
• Score: 17.285206913252786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or be natural language itself. This survey outlines the strategies used in the literature to build natural language state representations. We appeal for more linguistically interpretable and grounded representations, careful justification of design decisions and evaluation of the effectiveness of different approaches.

Related papers

• Learning telic-controllable state representations [3.072340427031969]
  We present a novel computational framework for state representation learning in bounded agents. Our work advances a unified theoretical perspective on goal-directed state representation learning in natural and artificial agents.
  arXiv  Detail & Related papers  (2024-06-20T16:38:25Z)
• Learning Language Structures through Grounding [8.437466837766895]
  We consider a family of machine learning tasks that aim to learn language structures through grounding. In Part I, we consider learning syntactic parses through visual grounding. In Part II, we propose two execution-aware methods to map sentences into corresponding semantic structures. In Part III, we propose methods that learn language structures from annotations in other languages.
  arXiv  Detail & Related papers  (2024-06-14T02:21:53Z)
• Learning with Language-Guided State Abstractions [58.199148890064826]
  Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations. Our method, LGA, uses a combination of natural language supervision and background knowledge from language models to automatically build state representations tailored to unseen tasks. Experiments on simulated robotic tasks show that LGA yields state abstractions similar to those designed by humans, but in a fraction of the time.
  arXiv  Detail & Related papers  (2024-02-28T23:57:04Z)
• BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models [56.93604813379634]
  Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels. We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
  arXiv  Detail & Related papers  (2023-06-02T12:54:38Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [56.85319224208865]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed. Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• Language Models as Inductive Reasoners [125.99461874008703]
  We propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts. We create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language. We provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts.
  arXiv  Detail & Related papers  (2022-12-21T11:12:14Z)
• An Inclusive Notion of Text [69.36678873492373]
  We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
  arXiv  Detail & Related papers  (2022-11-10T14:26:43Z)
• Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
  Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions. Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
  arXiv  Detail & Related papers  (2022-10-14T02:35:19Z)
• A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference [28.949004915740776]
  We present a multilingual approach for evaluating attribution methods for the Natural Language Inference (NLI) task in terms of faithfulness and plausibility. First, we introduce a novel cross-lingual strategy to measure faithfulness based on word alignments, which eliminates the drawbacks of erasure-based evaluations. We then perform a comprehensive evaluation of attribution methods, considering different output mechanisms and aggregation methods.
  arXiv  Detail & Related papers  (2022-04-11T22:11:05Z)
• Sentiment Analysis with Contextual Embeddings and Self-Attention [3.0079490585515343]
  In natural language the intended meaning of a word or phrase is often implicit and depends on the context. We propose a simple yet effective method for sentiment analysis using contextual embeddings and a self-attention mechanism. The experimental results for three languages, including morphologically rich Polish and German, show that our model is comparable to or even outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-12T02:19:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.