Neural semi-Markov CRF for Monolingual Word Alignment

• URL: http://arxiv.org/abs/2106.02569v1
• Date: Fri, 4 Jun 2021 16:04:00 GMT
• Title: Neural semi-Markov CRF for Monolingual Word Alignment
• Authors: Wuwei Lan, Chao Jiang, Wei Xu
• Abstract summary: We present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models.
• Score: 20.897157172049877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks.

Related papers

• Unsupervised Lexical Simplification with Context Augmentation [55.318201742039]
  Given a target word and its context, our method generates substitutes based on the target context and additional contexts sampled from monolingual data. We conduct experiments in English, Portuguese, and Spanish on the TSAR-2022 shared task, and show that our model substantially outperforms other unsupervised systems across all languages.
  arXiv  Detail & Related papers  (2023-11-01T05:48:05Z)
• Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation [49.89831914386982]
  We propose a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. Our approach outperforms the pre-training of plain text using only 1/4 of the data.
  arXiv  Detail & Related papers  (2021-09-02T16:05:24Z)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)
• Exemplar-Controllable Paraphrasing and Translation using Bitext [57.92051459102902]
  We adapt models from prior work to be able to learn solely from bilingual text (bitext) Our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions.
  arXiv  Detail & Related papers  (2020-10-12T17:02:50Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)
• Improving Language Generation with Sentence Coherence Objective [4.997730662279843]
  Existing models are often prone to output paragraphs of texts that gradually diverge from the given prompt. The goal of our project is to improve the coherence and consistency across sentences in a language-generation model.
  arXiv  Detail & Related papers  (2020-09-07T06:10:03Z)
• IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection [1.2301855531996841]
  Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier. The proposed approach shows an improvement in the system performance as compared to the Bi-LSTM based neural classifier.
  arXiv  Detail & Related papers  (2020-06-25T14:59:47Z)
• Neural CRF Model for Sentence Alignment in Text Simplification [31.62648025127563]
  We create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia. Experiments demonstrate that our proposed approach outperforms all the previous work on monolingual sentence alignment task by more than 5 points in F1. A Transformer-based seq2seq model trained on our datasets establishes a new state-of-the-art for text simplification in both automatic and human evaluation.
  arXiv  Detail & Related papers  (2020-05-05T16:47:51Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

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.