Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIterative GNN-based Decoder for Question Generation
وحدة فك الترميز القائم على GNN لتوليد السؤال
304
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تهدف توليد السؤال الطبيعي (QG) إلى توليد أسئلة من مقطع، ويتم الرد على الأسئلة التي تم إنشاؤها من المقطع.معظم النماذج مع نموذج الأداء الحديث النص الذي تم إنشاؤه سابقا في كل خطوة فك التشفير.ومع ذلك، (1) يتجاهلون معلومات الهيكل الغني المخفية في النص الذي تم إنشاؤه سابقا.(2) يتجاهلون تأثير الكلمات المنسوخة على مرور.ندرك أن المعلومات في الكلمات التي تم إنشاؤها مسبقا بمثابة معلومات مساعدة في الجيل اللاحق.لمعالجة هذه المشكلات، نقوم بتصميم وحدة فك الترميز المستندة إلى شبكة الرسم البياني للتكرار (IGND) لنموذج الجيل السابق باستخدام شبكة عصبية رسم بيانية في كل خطوة فك التشفير.علاوة على ذلك، يلتقط نموذج الرسم البياني لدينا علاقات التبعية في المقطع الذي يعزز الجيل.توضح النتائج التجريبية أن نموذجنا يتفوق على النماذج الحديثة مع مهام QG على مستوى الجملة على مجموعات بيانات الفريق وماركو.
Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.
References used
https://aclanthology.org/
rate research
Read More
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large
KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response
generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.
Due to efficient end-to-end training and fluency in generated texts, several encoder-decoder framework-based models are recently proposed for data-to-text generations. Appropriate encoding of input data is a crucial part of such encoder-decoder model
s. However, only a few research works have concentrated on proper encoding methods. This paper presents a novel encoder-decoder based data-to-text generation model where the proposed encoder carefully encodes input data according to underlying structure of the data. The effectiveness of the proposed encoder is evaluated both extrinsically and intrinsically by shuffling input data without changing meaning of that data. For selecting appropriate content information in encoded data from encoder, the proposed model incorporates attention gates in the decoder. With extensive experiments on WikiBio and E2E dataset, we show that our model outperforms the state-of-the models and several standard baseline systems. Analysis of the model through component ablation tests and human evaluation endorse the proposed model as a well-grounded system.
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.
Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling. In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee. It
is thus important for the counsellor chatbot to encourage the user to open up and talk. One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions. This paper applies models from two closely related NLP tasks --- summarization and question generation --- to restatement and question generation in the counselling context. We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety. We obtained the best performance in both restatement and question generation by fine-tuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.
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
