Iterative Document-level Information Extraction via Imitation Learning

• URL: http://arxiv.org/abs/2210.06600v3
• Date: Mon, 1 May 2023 04:17:28 GMT
• Title: Iterative Document-level Information Extraction via Imitation Learning
• Authors: Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron Steven White, Benjamin Van Durme
• Abstract summary: We present a novel iterative extraction model, IterX, for extracting complex relations. Our imitation learning approach casts the problem as a Markov decision process (MDP) It leads to state-of-the-art results on two established benchmarks.
• Score: 32.012467653148846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i.e., N-tuples representing a mapping from named slots to spans of text) within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template's slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks -- 4-ary relation extraction on SciREX and template extraction on MUC-4 -- as well as a strong baseline on the new BETTER Granular task.

Related papers

• Detection and Measurement of Syntactic Templates in Generated Text [58.111650675717414]
  We offer an analysis of syntactic features to characterize general repetition in models. We find that models tend to produce templated text in downstream tasks at a higher rate than what is found in human-reference texts.
  arXiv  Detail & Related papers  (2024-06-28T19:34:23Z)
• A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation [67.98367574025797]
  Existing syntactically-controlled paraphrase generation models perform promisingly with human-annotated or well-chosen syntactic templates. The prohibitive cost makes it unfeasible to manually design decent templates for every source sentence. We propose a novel Quality-based Syntactic Template Retriever (QSTR) to retrieve templates based on the quality of the to-be-generated paraphrases.
  arXiv  Detail & Related papers  (2023-10-20T03:55:39Z)
• Zero-shot Triplet Extraction by Template Infilling [13.295751492744081]
  Triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. We show that by reducing triplet extraction to a template infilling task over a pre-trained language model (LM), we can equip the extraction model with zero-shot learning capabilities. We propose a novel framework, ZETT, that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations.
  arXiv  Detail & Related papers  (2022-12-21T00:57:24Z)
• Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents [1.6249267147413522]
  This paper introduces a new information extraction model for business documents. It takes into account advantage of both span extraction and sequence labeling. The model is trained end-to-end to jointly optimize the two tasks.
  arXiv  Detail & Related papers  (2022-05-26T15:37:24Z)
• An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels [55.06990011183662]
  We introduce a new method for selecting prompt templates textitwithout labeled examples and textitwithout direct access to the model. Across 8 datasets representing 7 distinct NLP tasks, we show that when a template has high mutual information, it also has high accuracy on the task.
  arXiv  Detail & Related papers  (2022-03-21T21:51:43Z)
• Document-level Entity-based Extraction as Template Generation [13.110360825201044]
  We propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE) We first formulate them as a template generation problem, allowing models to efficiently capture cross-entity dependencies. A novel cross-attention guided copy mechanism, TopK Copy, is incorporated into a pre-trained sequence-to-sequence model to enhance the capabilities of identifying key information.
  arXiv  Detail & Related papers  (2021-09-10T14:18:22Z)
• Key Information Extraction From Documents: Evaluation And Generator [3.878105750489656]
  This research project compares state-of-the-art models for information extraction from documents. The results have shown that NLP based pre-processing is beneficial for model performance. The use of a bounding box regression decoder increases the model performance only for fields that do not follow a rectangular shape.
  arXiv  Detail & Related papers  (2021-06-09T16:12:21Z)
• GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction [134.5580003327839]
  We introduce a generative transformer-based encoder-decoder framework (GRIT) to model context at the document level. We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work.
  arXiv  Detail & Related papers  (2020-08-21T01:07:36Z)
• ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages [66.45377533562417]
  We propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage.
  arXiv  Detail & Related papers  (2020-05-14T16:15:58Z)
• Extraction of Templates from Phrases Using Sequence Binary Decision Diagrams [3.867363075280544]
  This paper presents an unsupervised approach for extracting templates from only tagged text by using a novel relaxed variant of the Sequence Binary Decision Diagram (SeqBDD) The main contribution of this paper is a relaxed form of the SeqBDD construction algorithm that enables it to form general representations from a small amount of data. Experiments show that the method is capable of high-quality extraction on tasks based on verb+preposition templates from corpora and phrasal templates from short messages from social media.
  arXiv  Detail & Related papers  (2020-01-28T05:30:53Z)

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.