Incomplete Utterance Rewriting as Semantic Segmentation

• URL: http://arxiv.org/abs/2009.13166v1
• Date: Mon, 28 Sep 2020 09:29:49 GMT
• Title: Incomplete Utterance Rewriting as Semantic Segmentation
• Authors: Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, Dongmei Zhang
• Abstract summary: We present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Our approach is four times faster than the standard approach in inference.
• Score: 57.13577518412252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.

Related papers

• Compositional Generalization for Data-to-Text Generation [86.79706513098104]
  We propose a novel model that addresses compositional generalization by clustering predicates into groups. Our model generates text in a sentence-by-sentence manner, relying on one cluster of predicates at a time. It significantly outperforms T5baselines across all evaluation metrics.
  arXiv  Detail & Related papers  (2023-12-05T13:23:15Z)
• Document-Level Abstractive Summarization [0.0]
  We study how efficient Transformer techniques can be used to improve the automatic summarization of very long texts. We propose a novel retrieval-enhanced approach which reduces the cost of generating a summary of the entire document by processing smaller chunks.
  arXiv  Detail & Related papers  (2022-12-06T14:39:09Z)
• Text Revision by On-the-Fly Representation Optimization [76.11035270753757]
  Current state-of-the-art methods formulate these tasks as sequence-to-sequence learning problems. We present an iterative in-place editing approach for text revision, which requires no parallel data. It achieves competitive and even better performance than state-of-the-art supervised methods on text simplification.
  arXiv  Detail & Related papers  (2022-04-15T07:38:08Z)
• Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing [110.4684789199555]
  We introduce scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" This formulation enables us to isolate coarse-grained and fine-grained aspects of the task, each of which we solve with off-the-shelf neural modules. Our model is modular, differentiable, interpretable, and allows us to garner extra supervision from scenarios.
  arXiv  Detail & Related papers  (2022-02-02T08:00:21Z)
• Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents [1.7491858164568674]
  This work presents the first approach that adopts the transformer networks for named entity recognition in handwritten documents. We achieve the new state-of-the-art performance in the ICDAR 2017 Information Extraction competition using the Esposalles database.
  arXiv  Detail & Related papers  (2021-12-08T09:26:21Z)
• Towards the Unseen: Iterative Text Recognition by Distilling from Errors [41.43280922432707]
  Prior arts mostly struggle with recognising unseen (or rarely seen) character sequences. We put forward a novel framework to tackle this "unseen" problem. Key to our success is a unique cross-modal variational autoencoder.
  arXiv  Detail & Related papers  (2021-07-26T10:06:42Z)
• Topical Change Detection in Documents via Embeddings of Long Sequences [4.13878392637062]
  We formulate the task of text segmentation as an independent supervised prediction task. By fine-tuning on paragraphs of similar sections, we are able to show that learned features encode topic information. Unlike previous approaches, which mostly operate on sentence-level, we consistently use a broader context.
  arXiv  Detail & Related papers  (2020-12-07T12:09:37Z)
• Automated Concatenation of Embeddings for Structured Prediction [75.44925576268052]
  We propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model.
  arXiv  Detail & Related papers  (2020-10-10T14:03:20Z)
• Cross-Thought for Sentence Encoder Pre-training [89.32270059777025]
  Cross-Thought is a novel approach to pre-training sequence encoder. We train a Transformer-based sequence encoder over a large set of short sequences. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders.
  arXiv  Detail & Related papers  (2020-10-07T21:02:41Z)

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.