Distilling Knowledge for Fast Retrieval-based Chat-bots

• URL: http://arxiv.org/abs/2004.11045v1
• Date: Thu, 23 Apr 2020 09:41:37 GMT
• Title: Distilling Knowledge for Fast Retrieval-based Chat-bots
• Authors: Amir Vakili Tahami, Kamyar Ghajar, Azadeh Shakery
• Abstract summary: We propose a new cross-encoder architecture and transfer knowledge from this model to a bi-encoder model using distillation. This effectively boosts bi-encoder performance at no cost during inference time.
• Score: 6.284464997330884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Response retrieval is a subset of neural ranking in which a model selects a suitable response from a set of candidates given a conversation history. Retrieval-based chat-bots are typically employed in information seeking conversational systems such as customer support agents. In order to make pairwise comparisons between a conversation history and a candidate response, two approaches are common: cross-encoders performing full self-attention over the pair and bi-encoders encoding the pair separately. The former gives better prediction quality but is too slow for practical use. In this paper, we propose a new cross-encoder architecture and transfer knowledge from this model to a bi-encoder model using distillation. This effectively boosts bi-encoder performance at no cost during inference time. We perform a detailed analysis of this approach on three response retrieval datasets.

Related papers

• Triple-Encoders: Representations That Fire Together, Wire Together [51.15206713482718]
  Contrastive Learning is a representation learning method that encodes relative distances between utterances into the embedding space via a bi-encoder. This study introduces triple-encoders, which efficiently compute distributed utterance mixtures from these independently encoded utterances. We find that triple-encoders lead to a substantial improvement over bi-encoders, and even to better zero-shot generalization than single-vector representation models.
  arXiv  Detail & Related papers  (2024-02-19T18:06:02Z)
• RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation [30.245143345565758]
  We propose a new retrieval-enhanced approach for personalized response generation. We design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. We quantitatively evaluate our model's performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.
  arXiv  Detail & Related papers  (2023-06-12T16:10:21Z)
• ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference [70.36083572306839]
  This paper proposes a new training and inference paradigm for re-ranking. We finetune a pretrained encoder-decoder model using in the form of document to query generation. We show that this encoder-decoder architecture can be decomposed into a decoder-only language model during inference.
  arXiv  Detail & Related papers  (2022-04-25T06:26:29Z)
• Human-Object Interaction Detection via Disentangled Transformer [63.46358684341105]
  We present Disentangled Transformer, where both encoder and decoder are disentangled to facilitate learning of two sub-tasks. Our method outperforms prior work on two public HOI benchmarks by a sizeable margin.
  arXiv  Detail & Related papers  (2022-04-20T08:15:04Z)
• A Speaker-aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-turn Dialogue Generation [13.820298189734686]
  This paper presents a novel open-domain dialogue generation model emphasizing the differentiation of speakers in multi-turn conversations. Our empirical results show that PHAED outperforms the state-of-the-art in both automatic and human evaluations.
  arXiv  Detail & Related papers  (2021-10-13T16:08:29Z)
• Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning [27.04857039060308]
  We propose to combine the best of both worlds to build a retrieval system. We employ a fast bi-encoder to replace the traditional feature-based pre-retrieval model. We train the pre-retrieval model and the re-ranking model at the same time via mutual learning.
  arXiv  Detail & Related papers  (2021-10-01T01:32:33Z)
• Question Answering Infused Pre-training of General-Purpose Contextualized Representations [70.62967781515127]
  We propose a pre-training objective based on question answering (QA) for learning general-purpose contextual representations. We accomplish this goal by training a bi-encoder QA model, which independently encodes passages and questions, to match the predictions of a more accurate cross-encoder model. We show large improvements over both RoBERTa-large and previous state-of-the-art results on zero-shot and few-shot paraphrase detection.
  arXiv  Detail & Related papers  (2021-06-15T14:45:15Z)
• A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems [15.654119998970499]
  We propose a template-guided hybrid pointer network for the knowledge-based task-oriented dialogue system. We design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response.
  arXiv  Detail & Related papers  (2021-06-10T15:49:26Z)
• Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems [53.160025961101354]
  We propose to train the generation model in a bidirectional manner by adding a backward reasoning step to the vanilla encoder-decoder training. The proposed backward reasoning step pushes the model to produce more informative and coherent content. Our method can improve response quality without introducing side information.
  arXiv  Detail & Related papers  (2021-04-30T20:38:27Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

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.