Manhattan Siamese LSTM for Question Retrieval in Community Question Answering

Othman, Nouha; Faiz, Rim; Smaïli, Kamel

doi:10.1007/978-3-030-33246-4_41

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…In order to improve the QR task, we propose an Attentive Siamese LSTM approach for question retrieval, referred to as ASLSTM to retrieve the semantically similar questions in cQA [21]. As illustrated in Figure 1, our approach is composed of three main modules namely, question preprocessing, word embedding learning and Manhattan LSTM (MaLSTM).…”

Section: Description Of the Proposed Aslstm Approachmentioning

confidence: 99%

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Othman

Faiz

Smaïli

2022

Data & Knowledge Engineering

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In this paper, we tackle the task of similar question retrieval (QR) which is essential for Commu-nity Question Answering (cQA) and aims to retrieve historical questions that are semantically equivalent to the new queries. Over time, with the sharp increase of community archives and the accumulation of duplicated questions, the QR problem has become increasingly challenging due to the shortness of the community questions as well as the word mismatch problem as users can formulate the same query using different wording. Although many efforts have been devoted to address this problem, existing methods mostly relied on supervised models which significantly depend on massive training data sets and manual feature engineering. Such methods are chiefly constrained by their specificities that ignore the word order and do not capture enough syntactic and semantic information in questions. In this paper, we rely on Neural Networks (NNs) which use a deep analysis of words and questions to take into consideration the semantics as well as the structure of questions to predict the semantic text similarity. We propose a deep learning approach based on a Siamese architecture with Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism to let the model give different words different attention while modeling questions. We also explore the use of Convolutional Neural Networks (CNN) nested within the Siamese architecture to retrieve relevant questions. Different similarity measures were tested to predict the semantic similarity between the the pairs of questions. To evaluate the proposed approach, we conducted experiments on large-scale datasets in English and Arabic.

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Section: Description Of the Proposed Aslstm Approachmentioning

confidence: 99%

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Othman

Faiz

Smaïli

2022

Data & Knowledge Engineering

Self Cite

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“…One of the most important works in this category is the MaLSTM [12] method, which measures the similarity of two sentences using Manhattan distance similarity at the output layer of the Siamese LSTM network architecture. Similarly, Othma et al [29] conducted a study based on the principle of giving the closest semantic question and the relevant answer to the question asked by a user among the community questions and answers on the web for both English and Arabic, using Siamese LSTM and Manhattan vector distance. In benchmark tests performed on Yahoo Answers Dataset, successful results are obtained.…”

Section: Related Workmentioning

confidence: 99%

Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM

2021

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Automatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student's answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the ''Natural Language Processing'' course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.

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“…In (Othman et al, 2019) authors use word embeddings to capture semantic and syntactic information from textual contexts and vectorize the questions. The embedding vectors feed a Siamese LSTM neural network, and the similarity between the questions is obtained as the Manhattan distance of the final LSTM hidden states.…”

Section: State Of the Artmentioning

confidence: 99%

Technological troubleshooting based on sentence embedding with deep transformers

2021

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In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine.

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Manhattan Siamese LSTM for Question Retrieval in Community Question Answering

Cited by 14 publications

References 18 publications

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM

Technological troubleshooting based on sentence embedding with deep transformers

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