Articles

AbstractTo enrich the research about sketch modality a new task termed Sketchy Scene Captioning isproposed in this paper. This task aims to generate sentence-level and paragraph-level descrip-tions for a sketchy scene. The sentence-level description provides the salient semantics of asketchy scene while the paragraph-level description gives more details about the …...

AbstractMorphological analysis is a fundamental task in natural language processing and results can beapplied to different downstream tasks such as named entity recognition syntactic analysis andmachine translation. However there are many problems in morphological analysis such as lowaccuracy caused by a lack of resources. In this paper to alleviate the …...

AbstractExisting entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However they are still challenged by the heterogeneous topological neighborhood structures which could cause the models to produce different representations of counterpart entities. In the paper we propose a global entity …...

AbstractMachine reading comprehension (MRC) is a typical natural language processing (NLP)task and has developed rapidly in the last few years. Various reading comprehension datasets have been built to support MRC studies. However large-scale and high-quality datasets are rare due to the high complexity and huge workforce cost of making sucha …...

AbstractThis paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific event type. Currently there is no specific model to directly deal with the EER task. Previous namedentity recognition methods that …...

AbstractDistant supervision can generate large-scale relation classification data quickly and economi-cally. However a great number of noise sentences are introduced which can not express their labeled relations. By means of pre-trained language model BERT’s powerful function in this paper we propose a BERT-based semantic denoising approach for distantly supervised relation …...

AbstractFor text-level discourse analysis there are various discourse schemes but relatively few labeleddata because discourse research is still immature and it is labor-intensive to annotate the innerlogic of a text. In this paper we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by …...

AbstractZipf’s law is a succinct yet powerful mathematical law in linguistics. However the mean-ingfulness and units of the law have remained controversial. The current study usesonline video comments call “danmu comment” to investigate these two questions. Theresults are consistent with previous studies arguing Zipf’s law is subject to topical coher-ence. …...

AbstractThe irrelevant information in documents poses a great challenge for machine reading compre-hension (MRC). To deal with such a challenge current MRC models generally fall into twoseparate parts: evidence extraction and answer prediction where the former extracts the key evi-dence corresponding to the question and the latter predicts the answer …...

AbstractGuessWhat?! is a task-oriented visual dialogue task which has two players a guesser and anoracle. Guesser aims to locate the object supposed by oracle by asking several Yes/No questions which are answered by oracle. How to ask proper questions is crucial to achieve the final goal of the whole task. …...

AbstractAbstractive dialogue summarization is the task of capturing the highlights of a dialogue andrewriting them into a concise version. In this paper we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue un-derstanding and summary generation. In detail we consider utterance and commonsense knowl-edge …...

AbstractExtractive text summarization seeks to extract indicative sentences from a source document andassemble them to form a summary. Selecting salient but not redundant sentences has alwaysbeen the main challenge. Unlike the previous two-stage strategies this paper presents a unifiedend-to-end model learning to rerank the sentences by modeling salience and redundancy …...

AbstractThe predominant approach of visual question answering (VQA) relies on encoding the imageand question with a ”black box” neural encoder and decoding a single token into answers suchas ”yes” or ”no”. Despite this approach’s strong quantitative results it struggles to come up withhuman-readable forms of justification for the prediction process. …...

AbstractManifold ranking has been successfully applied in query-oriented multi-document summariza-tion. It not only makes use of the relationships among the sentences but also the relationships between the given query and the sentences. However the information of original query is often insufficient. So we present a query expansion method which is …...

AbstractFew-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype …...

AbstractQuestion generation (QG) is to generate natural and grammatical questions that can be answeredby a specific answer for a given context. Previous sequence-to-sequence models suffer from aproblem that asking high-quality questions requires commonsense knowledge as backgrounds which in most cases can not be learned directly from training data resulting in …...

AbstractEmotion cause analysis (ECA) aims to identify the potential causes behind certain emotions intext. Lots of ECA models have been designed to extract the emotion cause at the clause level. However in many scenarios only extracting the cause clause is ambiguous. To ease the problemin this paper we introduce multi-level …...

AbstractExposure bias and poor translation diversity are two common problems in neural machine trans-lation (NMT) which are caused by the general of the teacher forcing strategy for training inthe NMT models. Moreover the NMT models usually require the large-scale and high-quality parallel corpus. However Korean is a low resource language …...

AbstractEmotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself with-out combining the semantics of emotion categories for modeling. Emotion classification of mi-croblogs is a process of reading the content of microblogs and combining the semantics …...

AbstractMetaphor detection plays an important role in tasks such as machine translation and human-machine dialogue. As more users express their opinions on products or other topics on socialmedia through metaphorical expressions this task is particularly especially topical. Most of the research in this field focuses on English and there are …...