Improved intent classification based on context information using a windows-based approach

• URL: http://arxiv.org/abs/2411.06022v1
• Date: Sat, 09 Nov 2024 00:56:02 GMT
• Title: Improved intent classification based on context information using a windows-based approach
• Authors: Jeanfranco D. Farfan-Escobedo, Julio C. Dos Reis,
• Abstract summary: The intent classification task aims at identifying what a user is attempting to achieve from an utterance. Previous works use only the current utterance to predict the intent of a given query. We propose several approaches to investigate the role of contextual information for the intent classification task.
• Score: 0.0
• License:
• Abstract: Conversational systems have a Natural Language Understanding (NLU) module. In this module, there is a task known as an intent classification that aims at identifying what a user is attempting to achieve from an utterance. Previous works use only the current utterance to predict the intent of a given query and they do not consider the role of the context (one or a few previous utterances) in the dialog flow for this task. In this work, we propose several approaches to investigate the role of contextual information for the intent classification task. Each approach is used to carry out a concatenation between the dialogue history and the current utterance. Our intent classification method is based on a convolutional neural network that obtains effective vector representations from BERT to perform accurate intent classification using an approach window-based. Our experiments were carried out on a real-world Brazilian Portuguese corpus with dialog flows provided by Wavy global company. Our results achieved substantial improvements over the baseline, isolated utterances (without context), in three approaches using the user's utterance and system's response from previous messages as dialogue context.

Related papers

• Parameter-Efficient Conversational Recommender System as a Language Processing Task [52.47087212618396]
  Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items' semantic information, a language model for dialogue generation, and a recommendation module for ranking relevant items. In this paper, we represent items in natural language and formulate CRS as a natural language processing task.
  arXiv  Detail & Related papers  (2024-01-25T14:07:34Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding [68.94808536012371]
  We propose a tree-structured pre-trained conversation model, which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora. Our method can achieve new state-of-the-art results on the DialoGLUE benchmark consisting of seven datasets and four popular dialog understanding tasks.
  arXiv  Detail & Related papers  (2022-09-14T13:42:50Z)
• Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs [123.50636090341236]
  We propose to automatically convert background knowledge documents into document semantic graphs. Our document semantic graphs preserve sentence-level information through the use of sentence nodes and provide concept connections between sentences. Our experiments show that our semantic graph-based knowledge selection improves over sentence selection baselines for both the knowledge selection task and the end-to-end response generation task on HollE.
  arXiv  Detail & Related papers  (2022-06-15T04:51:32Z)
• Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context [8.59600111891194]
  We propose to jointly model historical and future information through the posterior regularization method. We optimize the KL distance between these to regularize our model during training. Experiments on two dialogue datasets validate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2022-03-07T09:58:50Z)
• Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals [28.530853447203434]
  A proactive dialogue system has the ability to proactively lead the conversation. Background knowledge is essential to enable smooth and natural transitions in dialogue. We propose a new multi-task learning framework for retrieval-based knowledge-grounded proactive dialogue.
  arXiv  Detail & Related papers  (2021-07-18T00:27:31Z)
• A Streaming End-to-End Framework For Spoken Language Understanding [11.58499117295424]
  We propose a streaming end-to-end framework that can process multiple intentions in an online and incremental way. We evaluate our solution on the Fluent Speech Commands dataset and the intent detection accuracy is about 97 % on all multi-intent settings.
  arXiv  Detail & Related papers  (2021-05-20T21:37:05Z)
• Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters [52.725200145600624]
  We propose KnowExpert to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters. Experimental results show that KnowExpert performs comparably with the retrieval-based baselines.
  arXiv  Detail & Related papers  (2021-05-13T12:33:23Z)
• Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification [81.80311855996584]
  We propose a novel intent classification framework that employs acoustic features extracted from a pretrained speech recognition system and linguistic features learned from a pretrained language model. We achieve 90.86% and 99.07% accuracy on ATIS and Fluent speech corpus, respectively.
  arXiv  Detail & Related papers  (2021-02-15T07:20:06Z)
• Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems [14.206866126142002]
  Goal-oriented dialog systems enable users to complete specific goals like requesting information about a movie or booking a ticket. We propose an approach for automatically creating a large corpus of annotated dialogs from a few thoroughly annotated sample dialogs and the dialog schema. We achieve 18? 50% relative accuracy on a held-out test set compared to a baseline dialog generation approach.
  arXiv  Detail & Related papers  (2020-11-16T19:39:15Z)
• Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition [14.81680798372891]
  We design a hierarchical model based on self-attention to capture intra-sentence and inter-sentence information. Based on the found that the length of dialog affects the performance, we introduce a new dialog segmentation mechanism.
  arXiv  Detail & Related papers  (2020-03-12T22:26:11Z)

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.