4 research outputs found
Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning
Inspired by the curvature of space-time (Einstein, 1921), we introduce Curved
Contrastive Learning (CCL), a novel representation learning technique for
learning the relative turn distance between utterance pairs in multi-turn
dialogues. The resulting bi-encoder models can guide transformers as a response
ranking model towards a goal in a zero-shot fashion by projecting the goal
utterance and the corresponding reply candidates into a latent space. Here the
cosine similarity indicates the distance/reachability of a candidate utterance
toward the corresponding goal. Furthermore, we explore how these
forward-entailing language representations can be utilized for assessing the
likelihood of sequences by the entailment strength i.e. through the cosine
similarity of its individual members (encoded separately) as an emergent
property in the curved space. These non-local properties allow us to imagine
the likelihood of future patterns in dialogues, specifically by
ordering/identifying future goal utterances that are multiple turns away, given
a dialogue context. As part of our analysis, we investigate characteristics
that make conversations (un)plannable and find strong evidence of planning
capability over multiple turns (in 61.56% over 3 turns) in conversations from
the DailyDialog (Li et al., 2017) dataset. Finally, we show how we achieve
higher efficiency in sequence modeling tasks compared to previous work thanks
to our relativistic approach, where only the last utterance needs to be encoded
and computed during inference.Comment: Accepted in ACL 2023 Finding
A Cancel Culture Corpus through the Lens of Natural Language Processing
Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture
A Cancel Culture Corpus through the Lens of Natural Language Processing
Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture
A Cancel Culture Corpus through the Lens of Natural Language Processing
Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture