Semantic Matching Problem Research Articles

EGLR: Two-staged Explanation Generation and Language Reasoning framework for commonsense question answering

In the field of natural language processing, strong commonsense reasoning abilities are often required for intelligent systems to excel in the task of commonsense question answering (QA). To enhance the interpretability of QA systems, a natural approach is to generate textual explanations in addition to the predicted answers, ensuring the understandability of the results. Recent efforts address this by using a prompted language model (LM) with frozen parameters to generate explanations in a few-shot manner. These explanations are then used as additional context to guide a finetuned LM in making the final decision. However, these methods still underutilize the semantic information embedded in the explanatory text. Consequently, the reasoning models tend to rely on word co-occurrence and the knowledge stored in the model itself rather than fully exploiting the explanations. Therefore, we propose a two-staged Explanation Generation and Language Reasoning Framework (EGLR), Our framework takes advantage of the in-context learning capability of LMs to generate explanations and reformulates the reasoning task based on explanations as a semantic matching problem. Through joint prompting and training, our model can select the most appropriate explanation by comparing multiple explanations. Experimental results on three public datasets demonstrate that our framework achieves superior performance on the full dataset while maintaining performance on out-of-domain scenarios.

Word-level dual channel with multi-head semantic attention interaction for community question answering

<abstract><p>The semantic matching problem detects whether the candidate text is related to a specific input text. Basic text matching adopts the method of statistical vocabulary information without considering semantic relevance. Methods based on Convolutional neural networks (CNN) and Recurrent networks (RNN) provide a more optimized structure that can merge the information in the entire sentence into a single sentence-level representation. However, these representations are often not suitable for sentence interactive learning. We design a multi-dimensional semantic interactive learning model based on the mechanism of multiple written heads in the transformer architecture, which not only considers the correlation and position information between different word levels but also further maps the representation of the sentence to the interactive three-dimensional space, so as to solve the problem and the answer can select the best word-level matching pair, respectively. Experimentally, the algorithm in this paper was tested on Yahoo! and StackEx open-domain datasets. The results show that the performance of the method proposed in this paper is superior to the previous CNN/RNN and BERT-based methods.</p></abstract>

Semantic Context-Aware Image Style Transfer.

To provide semantic image style transfer results which are consistent with human perception, transferring styles of semantic regions of the style image to their corresponding semantic regions of the content image is necessary. However, when the object categories between the content and style images are not the same, it is difficult to match semantic regions between two images for semantic image style transfer. To solve the semantic matching problem and guide the semantic image style transfer based on matched regions, we propose a novel semantic context-aware image style transfer method by performing semantic context matching followed by a hierarchical local-to-global network architecture. The semantic context matching aims to obtain the corresponding regions between the content and style images by using context correlations of different object categories. Based on the matching results, we retrieve semantic context pairs where each pair is composed of two semantically matched regions from the content and style images. To achieve semantic context-aware style transfer, a hierarchical local-to-global network architecture, which contains two sub-networks including the local context network and the global context network, is proposed. The former focuses on style transfer for each semantic context pair from the style image to the content image, and generates a local style transfer image storing the detailed style feature representations for corresponding semantic regions. The latter aims to derive the stylized image by considering the content, the style, and the intermediate local style transfer images, so that inconsistency between different corresponding semantic regions can be addressed and solved. The experimental results show that the stylized results using our method are more consistent with human perception compared with the state-of-the-art methods.

DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY

Semantic matching finds certain types of semantic relationships among schema/data constructs. Examples include entity matching, entity linking, coreference resolution, schema/ontology matching, semantic text similarity, textual entailment, question answering, tagging, etc. Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R\&D directions.

Deep Convolutional Neural Network Based Medical Concept Normalization

Medical concept normalization is a critical problem in biomedical research and clinical applications. In this article, we focus on normalizing diagnostic and operation names in Chinese discharge summaries to standard concepts, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization, heterogeneity of tasks and flexible input of disambiguation mentions pose critical challenges in our problem. We propose two models, the basic model and flexible model, to tackle these problems. The basic model solves the core problem (the first three challenges) in ambiguous mentions normalization, while the flexible model deals with flexible input of ambiguous mentions and further explores the correlation among them. Specifically, in the basic model, we present a general framework to disambiguate a diagnosis and its corresponding operation simultaneously, which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with a multi-task shared structure. We propose that the key to address non-standard expressions and the short-text problem is to incorporate a matching tensor with multiple granularities. Then a multi-view CNN is adopted to extract semantic matching patterns. Finally, the multi-task shared structure allows the model to exploit medical correlations between diagnosis and operation mentions to better perform disambiguation tasks. Subsequently, we design a flexible model based on the basic model. Specifically, we add a flexible attention layer to all procedure representation vectors, and then apply a flexible multi-task scheme to share the correlated information. Comprehensive experimental analysis indicates that our model outperforms existing baselines, demonstrating the effectiveness and robustness of our model.

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set (two times greater than baseline results).1

Applying deep matching networks to Chinese medical question answering: a study and a dataset

BackgroundMedical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. It can be attributed to the difficulty of Chinese text processing and the lack of large-scale datasets. To bridge the gap, this paper introduces a Chinese medical QA dataset and proposes effective methods for the task.MethodsWe first construct a large scale Chinese medical QA dataset. Then we leverage deep matching neural networks to capture semantic interaction between words in questions and answers. Considering that Chinese Word Segmentation (CWS) tools may fail to identify clinical terms, we design a module to merge the word segments and produce a new representation. It learns the common compositions of words or segments by using convolutional kernels and selects the strongest signals by windowed pooling.ResultsThe best performer among popular CWS tools on our dataset is found. In our experiments, deep matching models substantially outperform existing methods. Results also show that our proposed semantic clustered representation module improves the performance of models by up to 5.5% Precision at 1 and 4.9% Mean Average Precision.ConclusionsIn this paper, we introduce a large scale Chinese medical QA dataset and cast the task into a semantic matching problem. We also compare different CWS tools and input units. Among the two state-of-the-art deep matching neural networks, MatchPyramid performs better. Results also show the effectiveness of the proposed semantic clustered representation module.

SINGLE-SHOT SEMANTIC MATCHER FOR UNSEEN OBJECT DETECTION

Abstract. In this paper we combine the ideas of image matching, object detection, image retrieval and zero-shot learning for stating and solving the semantic matching problem. Semantic matcher takes two images (test and request) as input and returns detected objects (bounding boxes) on test image corresponding to semantic class represented by request (sample) image. We implement our single-shot semantic matcher CNN architecture based on GoogleNet and YOLO/DetectNet architectures. We propose the detection-by-request training and testing protocols for semantic matching algorithms. We train and test our CNN on the ILSVRC 2014 with 200 seen and 90 unseen classes and provide the real-time object detection with mAP 23 for seen and mAP 21 for unseen classes.

Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network

Medical concept normalization is a critical problem in biomedical research and clinical applications. In this paper, we focus on normalizing diagnostic and procedure names in Chinese discharge summaries to standard entities, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization and heterogeneity of tasks pose critical challenges in our problem. This paper presents a general framework which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with multi-task shared structure to tackle the two tasks simultaneously. We propose that the key to address non-standard expressions and short-text problem is to incorporate a matching tensor with multiple granularities. Then multi-view CNN is adopted to extract semantic matching patterns and learn to synthesize them from different views. Finally, multi-task shared structure allows the model to exploit medical correlations between disease and procedure names to better perform disambiguation tasks. Comprehensive experimental analysis indicates our model outperforms existing baselines which demonstrates the effectiveness of our model.

Architecting Service-Oriented Publish/Subscribe Applications: Data Model and Matching Algorithm

The existing publish/subscribe (Pub/Sub) systems can't match events with subscriptions based on the semantic of events. Focusing on the production development activities, this paper presented a data model for modeling an ontology-based hierarchical description framework, defined service and its classification characteristics, and analyzed an automatic generation method of building service ontologies for the semi-structured data that exist in the Internet. A subscription matching algorithm was proposed for solving the intelligent semantic matching problem between the subscription and services. The results show that the service-oriented applications guarantee the initial and availability matching of individualized demands and service capabilities.

Application of the Agent-Based Ontology Matching Technology in the Relational Database

As the relational database be the main data storage mode, but the traditional keyword based syntactic matching defects in precision and recall. This paper provides a ontology matching mechanisms for relational database, which realizes the semantic level of the data retrieval by using the intelligent search technology from Agent and Ontology. The mechanism using DCL domain ontology, SQC services query policy, SearchPolicyMap mapping to solve the problems as data object description, retrieval conditions description, and the mapping between those two description. And provided the preconditions for the semantic matching under the relational database. Solve the semantic matching problem of interaction between human and Agent by the WI interface and CM mechanism . Solve the problem of interaction between Agent and relational database by the service customization Interface SBI. Finally, solve the problem of semantic retrieval and quantitative calculation by querying adapter QA and core algorithm CMA. The mechanism has a strong practicality and application domains independent. Can implement a specific level semantics of relational database retrieval through the application domain ontology creation and mapping configuration.