Sentence Matching Research Articles

Teaching IELTS Reading Skills

This research describes the teaching of IELTS Reading for students. This research is library research. The analysis shows that the IELTS reading test evaluates participants' understanding of information and language, requiring them to synthesize, interpret, and respond to information. The test consists of 14 types of questions, including Choosing a Title, True False Not Given / Yes No Not Given, Matching Features, Matching Headings, Matching Paragraph Information, Matching Sentence Endings, Sentence Completion, List Selection, Multiple Choice, Short Answer, Summary Completion, Flow Chart Completion, Diagram Completion, and Table Completion. The resulting test is graded on a band scale from range from 0 (did not take the test) to 9 (proficient in English) which indicates students' understanding and ability to read in English. Understanding the types of questions on the IELTS is crucial for students to prepare for the test. However, the test requires precise abilities for success. Teachers can create lesson plans for teaching Reading IELTS to help students prepare for the test and prepare them for the test. To answer IELTS Reading questions, students need fast and accurate reading skills and the right strategy. Teaching reading skills for the IELTS test can be challenging, but several strategies can help teachers structure effective teaching. These include understanding the test format, analyzing previous questions, building academic vocabulary, teaching active reading, practicing explaining the main idea, teaching skimming and scanning techniques, managing class discussion and analysis, practicing with varied reading material, using an integrated approach, giving constructive feedback, simulating reading tests, giving homework, training students to compose an outline, and promoting flexibility in learning.

Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts

Tasks such as semantic search and clustering on crisis-related social media texts enhance our comprehension of crisis discourse, aiding decision-making and targeted interventions. Pre-trained language models have advanced performance in crisis informatics, but their contextual embeddings lack semantic meaningfulness. Although the CrisisTransformers family includes a sentence encoder to address the semanticity issue, it remains monolingual, processing only English texts. Furthermore, employing separate models for different languages leads to embeddings in distinct vector spaces, introducing challenges when comparing semantic similarities between multi-lingual texts. Therefore, we propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed crisis-related social media texts for over 50 languages, such that texts with similar meanings are in close proximity within the same vector space, irrespective of language diversity. Results in sentence encoding and sentence matching tasks are promising, suggesting these models could serve as robust baselines when embedding multi-lingual crisis-related social media texts. The models are publicly available at: https://huggingface.co/crisistransformers.

Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision

Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision

Syntactic-Informed Graph Networks for Sentence Matching

Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.

Insights into the time course of evidentiality processing in Turkish heritage speakers using a self-paced reading task

IntroductionStudies with heritage language speakers (HLS) have often used offline measurements, investigating the post-interpretive effects which emerge after processing has been completed. Relatively few studies have investigated heritage language processing using time-sensitive methods that allow the collection of evidence regarding real-time language processing rather than post-interpretive judgments. Using a self-paced-reading paradigm, we aimed to expand our understanding of HLS language processing by investigating evidentiality-the linguistic marking of information source, which is grammatically expressed in Turkish, but not in English.MethodParticipants were 54 bilingual speakers of Turkish and English: 24 HLS (English onset: 0-5 yrs) and 30 emigrant Turkish speakers (ES) who grew up in Turkey before emigrating to Australia (English onset = 6-17 yrs). Participants read sentences with evidential-marked verb forms that either matched or mismatched to the information source context. Word-by-word reading times and end-of-sentence acceptability judgment speed and accuracy were measured.ResultsThe results showed that although the HLS' responses were slower and less accurate than the ES in both reading times and end-of-sentence acceptability judgments, they showed similarities in online processing patterns. Both groups were faster at reading the mismatching sentences compared to the matching sentences; however, this pattern emerged during the time course of reading first for the indirect condition for the ES, and only later for the direct condition and for the HLS for both evidential conditions. Only HLS read faster in the target region with the direct evidential that is shown to be acquired earlier in childhood, than they did for the indirect evidential which is mastered later. In contrast, the end-of-sentence judgment data showed that while the ES group responded faster to matching direct sentences than matching indirect, this effect was missing for the HLS. Nevertheless, there were similar patterns for accuracy across evidential conditions: both groups were more accurate with the direct evidential.DiscussionOverall, the use of the self-paced-reading paradigm allowed insights into HLS' evidentiality processing above and beyond their generally slower and less accurate processing compared to the reference group. This study provides further evidence for differences in the patterns observed using online vs. post interpretive measures in HLS, reinforcing the importance of combining these methodologies for further understanding of HLS competence and performance.

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

AbstractThe current work proposes PooRaa‐Agri KG, an agricultural knowledge graph‐based simplified multilingual query system that works in real time to provide concise answers for agriculture‐based queries. The proposed approach accommodates real‐time and low‐resource queries in English and Hindi with a novel multi‐stage solution consisting of data pre‐processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query‐to‐reconstructed sentence matching, and machine translation as its sub‐modules. In this work, a novel combination of rule‐based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end‐to‐end communication in the proposed system, a similarity‐based query answer system is proposed in this work.

Dynamic Pruning of Regions for Image–Sentence Matching

Image–sentence matching is becoming increasingly essential in the integrated understanding of vision and language. Prior approaches apply a pre-trained detection model to extract region features and explore fine-grained relationships between image and sentence by aggregating the similarities of all region–word pairs. However, all images are represented by the same number of regions, regardless of their respective semantic complexity, which results in a large number of redundant regions interfering with semantic inference and bringing additional computational burden. To address the lack of flexibility in image representation and information redundancy, a novel method named Dynamic Pruning of Regions for Image–Sentence Matching (DPRM) is proposed to efficiently capture relationships between text and image. In particular, a dynamic region pruning module is presented to dynamically select the appropriate number of regions according to the semantic complexity of each image, thus pruning redundant regions and reducing superfluous computations. Moreover, an inter-modality refinement module is designed to refine the fine-grained relationships of region–word pairs by retaining meaningful interaction features and suppressing interference from redundant alignments, which learns the more accurate semantic correspondences. Extensive experiments on MSCOCO and Flickr30K datasets prove the superiority of DPRM compared with previous approaches.

SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT

Paraphrasing is a way to write sentences with other words with the same intent or purpose. Automatic paraphrase detection can be done using Natural Language Sentence Matching (NLSM) which is part of Natural Language Processing (NLP). NLP is a computational technique for processing text in general, while NLSM is used specifically to find the relationship between two sentences. With the development Neural Network (NN), nowadays NLP can be done more easily by computers. Many models for detecting and paraphrasing in English have been developed compared to Indonesian, which has less training data. This study proposes SPratama Model, which models paraphrase detection for Indonesian using a Recurrent Neural Network (RNN), namely Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The data used is "Quora Question Pairs" taken from Kaggle and translated into Indonesian using Google Translate. The results of this study indicate that the proposed model has an accuracy of around 80% for the detection of paraphrased sentences.

Multi-Scale Fine-Grained Alignments for Image and Sentence Matching

Image and sentence matching is a critical task to bridge the visual and textual discrepancy due to the heterogeneous modalities. Great progress has been made by exploring the coarse-grained relationships between images and sentences or fine-grained relationships between regions and words. However, how to fully excavate and exploit corresponding relations between these two modalities is still challenging. In this work, we propose a novel Multi-scale Fine-grained Alignments Network (MFA), which can effectively explore multi-scale visual-textual correspondences to facilitate bridging cross-modal discrepancy. Specifically, word-scale matching module is firstly utilized to mine the basic but fundamental correspondences between a single word and independent region. Then, we propose a phrase-scale matching module to explore the relations between objects with the constraint of attribute and corresponding region, which can further reserve more associated information. To cope with the complex interactions among multiple phrases and images, we design the relation-scale matching module to capture high-order semantics between two modalities. Moreover, each matching module includes visual aggregation and textual aggregations, which can ensure the bi-directional coupling of multi-scale semantics. Extensive qualitative and quantitative experiments on two challenging datasets including Flickr30 K and MSCOCO, show that the proposed method achieves superior performance compared with the existing methods.

Semantic Sentence Matching Based on Multiple Parallelly Organized Interaction Layers at Various Granularity Combinations With Two-Stage Aggregation Strategy

Semantic Sentence Matching Based on Multiple Parallelly Organized Interaction Layers at Various Granularity Combinations With Two-Stage Aggregation Strategy

A risk factor tracing method for LNG receiving terminals based on GAT and a bidirectional LSTM network

LNG terminals are important transfer stations for LNG storage, processing, and outbound transmission which involve intensive manual operation and safety management activities. Traditional safety evaluation methods can provide "cause-effect" information when investigating the traceability of incorrect manual operation. However, manual analysis and summarizing risk factor causality is time-consuming and labor-intensive, and there are inaccuracy problems. Usually, HAZOP meetings require safety, electrical, mechanical, instrumentation and other engineers to brainstorm and summarize the risk factors of LNG receiving terminals. This process is laborious and time-consuming, and there is no guarantee that the person will be able to record it completely every time. In this paper, a graph attention network (GAT) and bidirectional Long and Short-Term Memory Network (LSTM) deep integrated learning model is proposed. The model can automatically extract risk factor causal relationships. After starting with a risk factor text, the number type features and syntactic dependency graph features of the input text are fused to enhance the causal relationship traceability of incorrect personnel operation. Taking the unloading and storage processes as examples, in terms of the effect of automatic extraction risk factors, the integrated algorithm model improves the extraction accuracy by up to 7.1% compared with a single algorithm. In addition, the correct extraction rate of different cause-effect relationships increases by 21.57%, while the integrated algorithm considers sentence matching, improves the granularity of cause-effect information based on the traditional method, expands the root cause of risk factors, and the traceability results are consistent with on-site accident event results. At the same time, it is more conducive to accurately managing incorrect manual operations. In the model training sample size sensitivity analysis, the algorithm has a superior correct causal label extraction rate for different sample sizes when the number of hidden layer stacking layers is K= 2, which solves the problem of model adaptation for the text volume of LNG terminal risk factors.

Automatic video clip and mixing based on semantic sentence matching

Automatic video clip and mixing based on semantic sentence matching

MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network.

Aiming to overcome the shortcomings of the existing text matching algorithms, in this research, we have studied the related technologies of sentence matching and dialogue retrieval and proposed a multi-granularity matching model based on Siamese neural networks. This method considers both deep semantic similarity and shallow semantic similarity of input sentences to completely mine similar information between sentences. Moreover, to alleviate the problem of out of vocabulary in sentences, we have combined both word and character granularity in deep semantic similarity to further learn information. Finally, comparative experiments were carried out on the Chinese data set LCQMC. The experimental results confirm the effectiveness and generalization ability of this method, and several ablation experiments also show the importance of each part of the model.

Adversarial Cross-domain Community Question Retrieval

Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.

Efficient Image and Sentence Matching.

Recently, the accuracy of image and sentence matching has been continuously improved by larger and larger models. However, such large models not only need huge storage space but also slow down inference speed, which are not very suitable for low-cost devices in real-world applications. To our knowledge, this work makes the first attempt to improve the model efficiency in the context of image and sentence matching, and accordingly proposes a simple yet effective Whitened Similarity Distillation (WSD) method, which can distill cross-modal knowledge from a large teacher model to a small student model of both high efficiency and accuracy. The high efficiency is achieved by performing: 1) feature representation based on efficient backbone networks; and 2) similarity measurement in a fast N-to-N manner. However, the accuracy of such a student model is much worse than that of teacher model, because there exists very large variation inconsistency between two cross-modal similarity matrices of teacher and student models, which is hard to reduce during the similarity distillation. By performing two whitening-like transformations in the orthogonal space, the proposed WSD can reduce the large variation inconsistency more isotropically and is able to improve the accuracy of student model. We perform extensive experiments on two benchmark datasets and demonstrate the effectiveness of the proposed WSD. Compared with the teacher model, our distilled student model is 7× smaller (in model size) and 9× faster (in testing speed), only at the cost of 2% accuracy decrease.

Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

Computer Music Query by Humming Considering Subsequence Matching Algorithm

Facing massive music data, how to create its index in content-based retrieval to improve retrieval speed is a crucial research content. In the feature extraction from music content based on sentence and index creation, the subsequence matching algorithm of music melody is stored in the database. Database However, when users conduct query by humming (QBH), they may hum a piece of music containing multiple sentences for retrieval, which requires the generation of multiple sentence features. Hence, a solution to single sentence feature, multi-sentence conversion and matching is proposed and applied to the retrieval system, with an excellent retrieval effect. The relevant achievements are also applicable to the retrieval system of time series data with a similar structure.

A Novel Architecture with Separate Comparison and Interaction Modules for Chinese Semantic Sentence Matching

A Novel Architecture with Separate Comparison and Interaction Modules for Chinese Semantic Sentence Matching

MKPM: Multi keyword-pair matching for natural language sentences

Sentence matching is widely used in various natural language tasks, such as natural language inference, paraphrase identification and question answering. For these tasks, we need to understand the logical and semantic relationship between two sentences. Most current methods use all information within a sentence to build a model and hence determine its relationship to another sentence. However, the information contained in some sentences may cause redundancy or introduce noise, impeding the performance of the model. Therefore, we propose a sentence matching method based on multi keyword-pair matching (MKPM), which uses keyword pairs in two sentences to represent the semantic relationship between them, avoiding the interference of redundancy and noise. Specifically, we first propose a sentence-pair-based attention mechanism sp-attention to select the most important word pair from the two sentences as a keyword pair, and then propose a Bi-task architecture to model the semantic information of these keyword pairs. The Bi-task architecture is as follows: 1. In order to understand the semantic relationship at the word level between two sentences, we design a word-pair task (WP-Task), which uses these keyword pairs to complete sentence matching independently. 2. We design a sentence-pair task (SP-Task) to understand the sentence level semantic relationship between the two sentences by sentence denoising. Through the integration of the two tasks, our model can understand sentences more accurately from the two granularities of word and sentence. Experimental results show that our model can achieve state-of-the-art performance in several tasks. Our source code is publicly available1.

The Language Network Is Recruited but Not Required for Nonverbal Event Semantics.

The ability to combine individual concepts of objects, properties, and actions into complex representations of the world is often associated with language. Yet combinatorial event-level representations can also be constructed from nonverbal input, such as visual scenes. Here, we test whether the language network in the human brain is involved in and necessary for semantic processing of events presented nonverbally. In Experiment 1, we scanned participants with fMRI while they performed a semantic plausibility judgment task versus a difficult perceptual control task on sentences and line drawings that describe/depict simple agent-patient interactions. We found that the language network responded robustly during the semantic task performed on both sentences and pictures (although its response to sentences was stronger). Thus, language regions in healthy adults are engaged during a semantic task performed on pictorial depictions of events. But is this engagement necessary? In Experiment 2, we tested two individuals with global aphasia, who have sustained massive damage to perisylvian language areas and display severe language difficulties, against a group of age-matched control participants. Individuals with aphasia were severely impaired on the task of matching sentences to pictures. However, they performed close to controls in assessing the plausibility of pictorial depictions of agent-patient interactions. Overall, our results indicate that the left frontotemporal language network is recruited but not necessary for semantic processing of nonverbally presented events.