Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGenerating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
توليد السؤال الذاتي المحتوي على الذات والتركز على الأزواج عن طريق التعلم المقلد مكافأة
195
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
بدافع من جيل السؤال المقترح في أنظمة توصية أخبار المحادلات، نقترح نموذجا لتوليد أزواج الإجابات السؤال (أزواج ضمان الجودة) مع أسئلة ذاتية التركيز ذاتي ومقيد الطول، إجابات تلخص المادة.نبدأ بجمع مجموعة بيانات جديدة من المقالات الإخبارية مع أسئلة كعناوين واقترانها مع ملخصات طول متفاوتة.يتم استخدام هذه البيانات هذه البيانات لتعلم ملخصات إنتاج نموذج توليد QA للزوج كجابات توازن الرصيد بالإيجاز مع الاكتفاء بالاشتراك مع أسئلتها المقابلة.ثم نعزز عملية توليد زوج ضمان الجودة مع وظيفة مكافأة مختلفة لتخفيف تحيز التعرض، وهي مشكلة شائعة في توليد اللغة الطبيعية.يظهر كل من المقاييس التلقائية والتقييم البشري هذه أزواج ضمان الجودة بنجاح التقاط القابس المركزي للمقالات وتحقيق دقة عالية للإجابة.
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.
References used
https://aclanthology.org/
rate research
Read More
Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field. Thi
s paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE. To capture the important information of the input passage we first automatically generate (rather than extracting) keyphrases, thus this task is reduced to keyphrase-question-answer triplet joint generation. Accordingly, we propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively, and then apply the generated question and keyphrases to guide the generation of answers. To establish a solid benchmark, we build our model on the strong generative pre-training model. Experimental results show that our model makes great breakthroughs in the question-answer pair generation task. Moreover, we make a comprehensive analysis on our model, suggesting new directions for this challenging task.
In education, quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far,
the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.
Spoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel training schemes for spoken question answering with a self-supervised traini
ng stage and a contrastive representation learning stage. In the self-supervised stage, we propose three auxiliary self-supervised tasks, including utterance restoration, utterance insertion, and question discrimination, and jointly train the model to capture consistency and coherence among speech documents without any additional data or annotations. We then propose to learn noise-invariant utterance representations in a contrastive objective by adopting multiple augmentation strategies, including span deletion and span substitution. Besides, we design a Temporal-Alignment attention to semantically align the speech-text clues in the learned common space and benefit the SQA tasks. By this means, the training schemes can more effectively guide the generation model to predict more proper answers. Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. Our code will be publicly available after publication.
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web page
s (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.
Despite excellent performance on tasks such as question answering, Transformer-based architectures remain sensitive to syntactic and contextual ambiguities. Question Paraphrasing (QP) offers a promising solution as a means to augment existing dataset
s. The main challenges of current QP models include lack of training data and difficulty in generating diverse and natural questions. In this paper, we present Conquest, a framework for generating synthetic datasets for contextual question paraphrasing. To this end, Conquest first employs an answer-aware question generation (QG) model to create a question-pair dataset and then uses this data to train a contextualized question paraphrasing model. We extensively evaluate Conquest and show its ability to produce more diverse and fluent question pairs than existing approaches. Our contextual paraphrase model also establishes a strong baseline for end-to-end contextual paraphrasing. Further, We find that context can improve BLEU-1 score on contextual compression and expansion by 4.3 and 11.2 respectively, compared to a non-contextual model.
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
