Dense Paraphrasing for Textual Enrichment
- URL: http://arxiv.org/abs/2210.11563v1
- Date: Thu, 20 Oct 2022 19:58:31 GMT
- Title: Dense Paraphrasing for Textual Enrichment
- Authors: Jingxuan Tu, Kyeongmin Rim, Eben Holderness, James Pustejovsky
- Abstract summary: We define the process of rewriting a textual expression (lexeme or phrase) such that it reduces ambiguity while also making explicit the underlying semantics that is not (necessarily) expressed in the economy of sentence structure as Dense Paraphrasing (DP)
We build the first complete DP dataset, provide the scope and design of the annotation task, and present results demonstrating how this DP process can enrich a source text to improve inferencing and QA task performance.
- Score: 7.6233489924270765
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding inferences and answering questions from text requires more than
merely recovering surface arguments, adjuncts, or strings associated with the
query terms. As humans, we interpret sentences as contextualized components of
a narrative or discourse, by both filling in missing information, and reasoning
about event consequences. In this paper, we define the process of rewriting a
textual expression (lexeme or phrase) such that it reduces ambiguity while also
making explicit the underlying semantics that is not (necessarily) expressed in
the economy of sentence structure as Dense Paraphrasing (DP). We build the
first complete DP dataset, provide the scope and design of the annotation task,
and present results demonstrating how this DP process can enrich a source text
to improve inferencing and QA task performance. The data and the source code
will be publicly available.
Related papers
- Enhancing Argument Structure Extraction with Efficient Leverage of
Contextual Information [79.06082391992545]
We propose an Efficient Context-aware model (ECASE) that fully exploits contextual information.
We introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information.
Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance.
arXiv Detail & Related papers (2023-10-08T08:47:10Z) - Conjunct Resolution in the Face of Verbal Omissions [51.220650412095665]
We propose a conjunct resolution task that operates directly on the text and makes use of a split-and-rephrase paradigm in order to recover the missing elements in the coordination structure.
We curate a large dataset, containing over 10K examples of naturally-occurring verbal omissions with crowd-sourced annotations.
We train various neural baselines for this task, and show that while our best method obtains decent performance, it leaves ample space for improvement.
arXiv Detail & Related papers (2023-05-26T08:44:02Z) - PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and
Entailment Recognition [63.51569687229681]
We argue for the need to recognize the textual entailment relation of each proposition in a sentence individually.
We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters.
Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document.
arXiv Detail & Related papers (2022-12-21T04:03:33Z) - Discourse Analysis via Questions and Answers: Parsing Dependency
Structures of Questions Under Discussion [57.43781399856913]
This work adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis.
We characterize relationships between sentences as free-form questions, in contrast to exhaustive fine-grained questions.
We develop the first-of-its-kind QUD that derives a dependency structure of questions over full documents.
arXiv Detail & Related papers (2022-10-12T03:53:12Z) - A Survey of Implicit Discourse Relation Recognition [9.57170901247685]
implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective.
This article provides a comprehensive and up-to-date survey for the IDRR task.
arXiv Detail & Related papers (2022-03-06T15:12:53Z) - Text Simplification for Comprehension-based Question-Answering [7.144235435987265]
We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset.
We benchmark the newly created corpus and perform an ablation study for examining the effect of the simplification process in the SQuAD-based question answering task.
arXiv Detail & Related papers (2021-09-28T18:48:00Z) - Decontextualization: Making Sentences Stand-Alone [13.465459751619818]
Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context.
Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window.
We define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context.
arXiv Detail & Related papers (2021-02-09T22:52:37Z) - Pairwise Representation Learning for Event Coreference [73.10563168692667]
We develop a Pairwise Representation Learning (PairwiseRL) scheme for the event mention pairs.
Our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments.
We show that PairwiseRL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks.
arXiv Detail & Related papers (2020-10-24T06:55:52Z) - Pareto Probing: Trading Off Accuracy for Complexity [87.09294772742737]
We argue for a probe metric that reflects the fundamental trade-off between probe complexity and performance.
Our experiments with dependency parsing reveal a wide gap in syntactic knowledge between contextual and non-contextual representations.
arXiv Detail & Related papers (2020-10-05T17:27:31Z) - XTE: Explainable Text Entailment [8.036150169408241]
Entailment is the task of determining whether a piece of text logically follows from another piece of text.
XTE - Explainable Text Entailment - is a novel composite approach for recognizing text entailment.
arXiv Detail & Related papers (2020-09-25T20:49:07Z)
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.