Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBiSECT: Learning to Split and Rephrase Sentences with Bitexts
Bisect: تعلم تقسيم وجمل إعادة صياغة مع Bitex
511
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
مهمة مهمة في تطبيقات NLP مثل تبسيط الجملة هي القدرة على اتخاذ جملة طويلة ومعقدة وتقسيمها إلى جمل أقصر، وإعادة صياغة حسب الضرورة. نقدم مجموعة بيانات جديدة ونموذج جديد لهذه المهمة الانقسام وإعادة صياغة. تتكون بياناتنا في Bisect التدريبية من 1 مليون جمل إنجليزية طويلة مقترن بأجمل الإنجليزية الأقصر والمعاواة بينها. نحصل على هؤلاء من خلال استخراج محاذاة جملة واحدة في فورانيا متوازية ثنائية اللغة ثم استخدام الترجمة الآلية لتحويل كلا الجانبين من الجور إلى نفس اللغة. يحتوي Bisect على أمثلة تدريبية ذات جودة أعلى من SPORTA SPORTA السابق وإعادة صياغتها، مع انشقاقات الجملة التي تتطلب تعديلات أكثر أهمية. نقوم بتصنيف أمثلة في Corpus لدينا واستخدام هذه الفئات في نموذج جديد يتيح لنا استهداف مناطق محددة من جملة المدخلات التي سيتم تقسيمها وتحريرها. علاوة على ذلك، نوضح أن النماذج المدربة على Bisect يمكن أن تؤدي مجموعة متنوعة واسعة من العمليات المنقسمة وتحسينها على النهج السابقة للحالة السابقة في التقييمات التلقائية والبشرية.
An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this split and rephrase' task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than the previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.
References used
https://aclanthology.org/
rate research
Read More
Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have be
en proposed to alleviate this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for fine-tuning a pre-trained language model BART on the sentential paraphrasing task. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.
Split-and-rephrase is a challenging task that promotes the transformation of a given complex input sentence into multiple shorter sentences retaining equivalent meaning. This rewriting approach conceptualizes that shorter sentences benefit human read
ers and improve NLP downstream tasks attending as a preprocessing step. This work presents a complete pipeline capable of performing the split-and-rephrase method in a cross-lingual manner. We trained sequence-to-sequence neural models as from English corpora and applied them to predict the transformations in English and Brazilian Portuguese sentences jointly with BERT's masked language modeling. Contrary to traditional approaches that seek training models with extensive vocabularies, we present a non-trivial way to construct symbolic ones generalized solely by grammatical classes (POS tags) and their respective recurrences, reducing the amount of necessary training data. This pipeline contribution showed competitive results encouraging the expansion of the method to languages other than English.
Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag
of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them. Inspired by such an intuition, in this paper, we perform a study to investigate how order'' information takes effects in natural language learning. By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.
Paraphrase generation is an important task in natural language processing. Previous works focus on sentence-level paraphrase generation, while ignoring document-level paraphrase generation, which is a more challenging and valuable task. In this paper
, we explore the task of document-level paraphrase generation for the first time and focus on the inter-sentence diversity by considering sentence rewriting and reordering. We propose CoRPG (Coherence Relationship guided Paraphrase Generation), which leverages graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence, which can be used for re-arranging the multiple (possibly modified) input sentences. We create a pseudo document-level paraphrase dataset for training CoRPG. Automatic evaluation results show CoRPG outperforms several strong baseline models on the BERTScore and diversity scores. Human evaluation also shows our model can generate document paraphrase with more diversity and semantic preservation.
For voice assistants like Alexa, Google Assistant, and Siri, correctly interpreting users' intentions is of utmost importance. However, users sometimes experience friction with these assistants, caused by errors from different system components or us
er errors such as slips of the tongue. Users tend to rephrase their queries until they get a satisfactory response. Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g. users' implicit feedback). To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. We showcase how to leverage the dialogue context and user-agent interaction signals, including the user's implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
