scholarly journals Meaningful Answer Generation of E-Commerce Question-Answering

2021 ◽  
Vol 39 (2) ◽  
pp. 1-26
Author(s):  
Shen Gao ◽  
Xiuying Chen ◽  
Zhaochun Ren ◽  
Dongyan Zhao ◽  
Rui Yan
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Neural Model ◽  
Product Reviews ◽  
Neural Models ◽  
Product Attributes ◽  
Human Evaluation ◽  
Final Answer ◽  
Answer Pattern

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

scholarly journals Neural Program Induction for KBQA Without Gold Programs or Query Annotations

2019 ◽  
Author(s):  
Ghulam Ahmed Ansari ◽  
Amrita Saha ◽  
Vishwajeet Kumar ◽  
Mohan Bhambhani ◽  
Karthik Sankaranarayanan ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Neural Models ◽  
Input Program ◽  
Correct Program ◽  
Final Answer ◽  
High Level ◽  
Executable Programs ◽  
Complex Question

Neural Program Induction (NPI) is a paradigm for decomposing high-level tasks such as complex question-answering over knowledge bases (KBQA) into executable programs by employing neural models. Typically, this involves two key phases: i) inferring input program variables from the high-level task description, and ii) generating the correct program sequence involving these variables. Here we focus on NPI for Complex KBQA with only the final answer as supervision, and not gold programs. This raises major challenges; namely, i) noisy query annotation in the absence of any supervision can lead to catastrophic forgetting while learning, ii) reward becomes extremely sparse owing to the noise. To deal with these, we propose a noise-resilient NPI model, Stable Sparse Reward based Programmer (SSRP) that evades noise-induced instability through continual retrospection and its comparison with current learning behavior. On complex KBQA datasets, SSRP performs at par with hand-crafted rule-based models when provided with gold program input, and in the noisy settings outperforms state-of-the-art models by a significant margin even with a noisier query annotator.

scholarly journals PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Majid Asgari-Bidhendi ◽  
Mehrdad Nasser ◽  
Behrooz Janfada ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Relation Extraction ◽  
Neural Model ◽  
Semantic Relations ◽  
Base Population ◽  
Neural Models ◽  
Persian Language ◽  
Knowledge Base Population

Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, question answering, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big data in the Persian language for different applications. In this paper, we present “PERLEX” as the first Persian dataset for relation extraction, which is an expert-translated version of the “SemEval-2010-Task-8” dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual BERT contextual word representations. The experiments result in the maximum F1-score of 77.66% (provided by BERTEM-MTB method) as the state of the art of relation extraction in the Persian language.

scholarly journals A Goal-Driven Tree-Structured Neural Model for Math Word Problems

2019 ◽  
Author(s):  
Zhipeng Xie ◽  
Shichao Sun
Keyword(s):  
Word Problems ◽  
State Of The Art ◽  
Neural Model ◽  
Neural Models ◽  
Top Down ◽  
Feedforward Networks ◽  
Whole Process ◽  
Expression Tree ◽  
Generate Solution ◽  
Human Problem

Most existing neural models for math word problems exploit Seq2Seq model to generate solution expressions sequentially from left to right, whose results are far from satisfactory due to the lack of goal-driven mechanism commonly seen in human problem solving. This paper proposes a tree-structured neural model to generate expression tree in a goal-driven manner. Given a math word problem, the model first identifies and encodes its goal to achieve, and then the goal gets decomposed into sub-goals combined by an operator in a top-down recursive way. The whole process is repeated until the goal is simple enough to be realized by a known quantity as leaf node. During the process, two-layer gated-feedforward networks are designed to implement each step of goal decomposition, and a recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than the simple goals of subtrees. Experimental results on the dataset Math23K have shown that our tree-structured model outperforms significantly several state-of-the-art models.

scholarly journals A Model to Determine the Propagation Losses Based on the Integration of Hata-Okumura and Wavelet Neural Models

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Luis F. Pedraza ◽  
Cesar A. Hernández ◽  
Danilo A. López
Keyword(s):  
Cellular Network ◽  
Large Scale ◽  
Wireless Systems ◽  
Neural Model ◽  
Propagation Model ◽  
Neural Models ◽  
Mobile Cellular ◽  
Proposed Model ◽  
Propagation Losses ◽  
High Degree

Radioelectric spectrum occupancy forecast has proven useful for the design of wireless systems able to harness spectrum opportunities like cognitive radio. This paper proposes the development of a model that identifies propagation losses and spectrum opportunities in a channel of a mobile cellular network for an urban environment using received signal power forecast. The proposed model integrates the Hata-Okumura (H-O) large-scale propagation model with a wavelet neural model. The model results, obtained through simulations, show that the wavelet neural model forecasts with a high degree of precision, which is consistent with the observed behavior in experiments carried out in wireless systems of this type.

scholarly journals Location-Aware Graph Convolutional Networks for Video Question Answering

2020 ◽  
Vol 34 (07) ◽  
pp. 11021-11028 ◽  
Author(s):  
Deng Huang ◽  
Peihao Chen ◽  
Runhao Zeng ◽  
Qing Du ◽  
Mingkui Tan ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Video Frame ◽  
Location Information ◽  
Object Interaction ◽  
Location Aware ◽  
Art Methods ◽  
Final Answer ◽  
Video Question Answering

We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art methods attempt to apply spatio-temporal attention mechanism on video frame features without explicitly modeling the location and relations among object interaction occurred in videos. However, the relations between object interaction and their location information are very critical for both action recognition and question reasoning. In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction. Here, each node is associated with an object represented by its appearance and location features. Based on the constructed graph, we propose to use graph convolution to infer both the category and temporal locations of an action. As the graph is built on objects, our method is able to focus on the foreground action contents for better video question answering. Lastly, we leverage an attention mechanism to combine the output of graph convolution and encoded question features for final answer reasoning. Extensive experiments demonstrate the effectiveness of the proposed methods. Specifically, our method significantly outperforms state-of-the-art methods on TGIF-QA, Youtube2Text-QA and MSVD-QA datasets.

scholarly journals Neural models for information retrieval without labeled data

2019 ◽  
Vol 53 (2) ◽  
pp. 104-105
Author(s):  
Hamed Zamani
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Performance Prediction ◽  
Large Scale ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Training Data ◽  
Retrieval Model ◽  
Neural Models ◽  
Retrieval Models

Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval [9]. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or weakly supervised solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, in our weak supervision approach, neural models are trained on a large set of noisy and biased training data obtained from external resources, existing models, or heuristics. We first introduce relevance-based embedding models [3] that learn distributed representations for words and queries. We show that the learned representations can be effectively employed for a set of IR tasks, including query expansion, pseudo-relevance feedback, and query classification [1, 2]. We further propose a standalone learning to rank model based on deep neural networks [5, 8]. Our model learns a sparse representation for queries and documents. This enables us to perform efficient retrieval by constructing an inverted index in the learned semantic space. Our model outperforms state-of-the-art retrieval models, while performing as efficiently as term matching retrieval models. We additionally propose a neural network framework for predicting the performance of a retrieval model for a given query [7]. Inspired by existing query performance prediction models, our framework integrates several information sources, such as retrieval score distribution and term distribution in the top retrieved documents. This leads to state-of-the-art results for the performance prediction task on various standard collections. We finally bridge the gap between retrieval and recommendation models, as the two key components in most information systems. Search and recommendation often share the same goal: helping people get the information they need at the right time. Therefore, joint modeling and optimization of search engines and recommender systems could potentially benefit both systems [4]. In more detail, we introduce a retrieval model that is trained using user-item interaction (e.g., recommendation data), with no need to query-document relevance information for training [6]. Our solutions and findings in this dissertation smooth the path towards learning efficient and effective models for various information retrieval and related tasks, especially when large-scale training data is not available.

Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

2021 ◽  
Vol 2 (4) ◽  
pp. 1-24
Author(s):  
Pratyay Banerjee ◽  
Kuntal Kumar Pal ◽  
Murthy Devarakonda ◽  
Chitta Baral
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Neural Models ◽  
Named Entity ◽  
Input Text ◽  
The Impact ◽  
Biomedical Named Entity Recognition ◽  
Entity Class

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

scholarly journals Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

2020 ◽  
Vol 34 (05) ◽  
pp. 7651-7658 ◽  
Author(s):  
Yang Deng ◽  
Wai Lam ◽  
Yuexiang Xie ◽  
Daoyuan Chen ◽  
Yaliang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Reading Difficulties ◽  
Text Summarization ◽  
Essential Information ◽  
Joint Learning ◽  
Community Question Answering ◽  
Proposed Model ◽  
Correlation Information

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.

scholarly journals Neural Unsupervised Semantic Role Labeling

2021 ◽  
Vol 20 (6) ◽  
pp. 1-16
Author(s):  
Kashif Munir ◽  
Hai Zhao ◽  
Zuchao Li
Keyword(s):  
Argument Structure ◽  
State Of The Art ◽  
Neural Model ◽  
Semantic Structure ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Neural Models ◽  
Previous State ◽  
Dependency Relations ◽  
Predicate Argument Structure

The task of semantic role labeling ( SRL ) is dedicated to finding the predicate-argument structure. Previous works on SRL are mostly supervised and do not consider the difficulty in labeling each example which can be very expensive and time-consuming. In this article, we present the first neural unsupervised model for SRL. To decompose the task as two argument related subtasks, identification and clustering, we propose a pipeline that correspondingly consists of two neural modules. First, we train a neural model on two syntax-aware statistically developed rules. The neural model gets the relevance signal for each token in a sentence, to feed into a BiLSTM, and then an adversarial layer for noise-adding and classifying simultaneously, thus enabling the model to learn the semantic structure of a sentence. Then we propose another neural model for argument role clustering, which is done through clustering the learned argument embeddings biased toward their dependency relations. Experiments on the CoNLL-2009 English dataset demonstrate that our model outperforms the previous state-of-the-art baseline in terms of non-neural models for argument identification and classification.

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