Although mobile applications have been widely used, there is limited research on the acceptance of mobile-based applications for language learning among foreign learners. Hence, we propose an extended version of the T...
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To alleviate the error propagation in the traditional pipelined models for Abstract Meaning Representation (AMR) parsing, we formulate AMR parsing as a joint task that performs the two subtasks: concept identification...
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Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a...
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ISBN:
(纸本)9781665418683
Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper ***, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after,so there is no need to provide any context-related information after that. Thus, in addition to match the response with context,it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching *** results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.
Large language Models (LLMs) have made significant advancements in Natural languageprocessing (NLP) by excelling in various NLP tasks. This study specifically focuses on evaluating the performance of LLMs for Named E...
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In this paper, we formalize the task of finding a knowledge base entry that a given named entity mention refers to, namely entity linking, by identifying the most "important" node among the graph nodes repre...
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Word Sense Disambiguation (WSD) is one of the fundamental natural languageprocessing tasks. However, lack of training corpora is a bottleneck to construct a high accurate all-words WSD system. Annotating a large-scal...
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Recent research usually models POS tagging as a sequential labeling problem, in which only local context features can be used. Due to the lack of morphological inflections, many tagging ambiguities in Chinese are diff...
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Annotating Named Entity Recognition (NER) training corpora is a costly process but necessary for supervised NER systems. This paper presents an approach to generate large-scale Chinese NER training data from an Englis...
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We propose a general hierarchical vertical classification framework, which can automatically discover the inherent hierarchical structure of relationships among verticals based on flat datasets, and then build a hiera...
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With the rapidly growing amount of information available on the internet, recommender systems become popular tools to promote relevant online information to a given user. Although collaborative filtering is the most p...
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