Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning

• URL: http://arxiv.org/abs/2211.07591v2
• Date: Mon, 26 Jun 2023 18:05:48 GMT
• Title: Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning
• Authors: Justus-Jonas Erker, Stefan Schaffer, Gerasimos Spanakis
• Abstract summary: 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.
• Score: 2.5567566997688043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization [48.284512017469524]
  Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations. Traditional language models often overlook the distinct features of these dialogues by treating them as regular text. We propose a speaker-enhanced pre-training method for long dialogue summarization.
  arXiv  Detail & Related papers  (2024-01-31T04:50:00Z)
• Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation [52.270712965271656]
  We propose a new model of contextual word representation, not from a neural perspective, but from a purely syntactic and probabilistic perspective. We find that the graph of our model resembles transformers, with correspondences between dependencies and self-attention. Experiments show that our model performs competitively to transformers on small to medium sized datasets.
  arXiv  Detail & Related papers  (2023-11-26T06:56:02Z)
• Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm [0.0]
  Current neural network models of dialogue generation show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.
  arXiv  Detail & Related papers  (2022-12-28T22:46:57Z)
• Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion [89.01668641930206]
  We present a framework for modeling interactional communication in dyadic conversations. We autoregressively output multiple possibilities of corresponding listener motion. Our method organically captures the multimodal and non-deterministic nature of nonverbal dyadic interactions.
  arXiv  Detail & Related papers  (2022-04-18T17:58:04Z)
• Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension [48.483910831143724]
  Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances. We develop a novel narrative-guided pre-training strategy that learns by narrating the key information from a dialogue input.
  arXiv  Detail & Related papers  (2022-03-19T05:20:25Z)
• Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance [55.10864476206503]
  We investigate the use of quantized vectors to model the latent linguistic embedding. By enforcing different policies over the latent spaces in the training, we are able to obtain a latent linguistic embedding. Our experiments show that the voice cloning system built with vector quantization has only a small degradation in terms of perceptive evaluations.
  arXiv  Detail & Related papers  (2021-06-25T07:51:35Z)
• $C^3$: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues [97.25466640240619]
  Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. We propose a novel approach of Compositional Counterfactual Contrastive Learning to develop contrastive training between factual and counterfactual samples in video-grounded dialogues.
  arXiv  Detail & Related papers  (2021-06-16T16:05:27Z)
• CloneBot: Personalized Dialogue-Response Predictions [0.0]
  The project task was to create a model that, given a speaker ID, chat history, and an utterance query, can predict the response utterance in a conversation. The model is personalized for each speaker. This task can be a useful tool for building speech bots that talk in a human-like manner in a live conversation.
  arXiv  Detail & Related papers  (2021-03-31T01:15:37Z)
• Multi-turn Response Selection using Dialogue Dependency Relations [39.99448321736736]
  Multi-turn response selection is a task designed for developing dialogue agents. We propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Our model outperforms the state-of-the-art baselines on both D7 and DSTC8*, with competitive results on Ubuntu.
  arXiv  Detail & Related papers  (2020-10-04T08:00:19Z)

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.