97 research outputs found
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
Word representation has always been an important research area in the history
of natural language processing (NLP). Understanding such complex text data is
imperative, given that it is rich in information and can be used widely across
various applications. In this survey, we explore different word representation
models and its power of expression, from the classical to modern-day
state-of-the-art word representation language models (LMS). We describe a
variety of text representation methods, and model designs have blossomed in the
context of NLP, including SOTA LMs. These models can transform large volumes of
text into effective vector representations capturing the same semantic
information. Further, such representations can be utilized by various machine
learning (ML) algorithms for a variety of NLP related tasks. In the end, this
survey briefly discusses the commonly used ML and DL based classifiers,
evaluation metrics and the applications of these word embeddings in different
NLP tasks
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when
rewards are sparse. One common solution is to use intrinsic rewards to
encourage agents to explore their environment. However, recent intrinsic
exploration methods often use state-based novelty measures which reward
low-level exploration and may not scale to domains requiring more abstract
skills. Instead, we explore natural language as a general medium for
highlighting relevant abstractions in an environment. Unlike previous work, we
evaluate whether language can improve over existing exploration methods by
directly extending (and comparing to) competitive intrinsic exploration
baselines: AMIGo (Campero et al., 2021) and NovelD (Zhang et al., 2021). These
language-based variants outperform their non-linguistic forms by 47-85% across
13 challenging tasks from the MiniGrid and MiniHack environment suites.Comment: NeurIPS 202
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