作者: anesthesia 時間: 2025-3-21 20:26
Learning on?Structured Documents for?Conditional Question Answeringined with LSD on massive unlabeled structured documents and is fine-tuned on labeled CQA dataset afterwards. To overcome the limitation of outputting answers with complex formats in CQA, we propose a pipeline that enables the generation of multiple answers and conditions. Experimental results on the作者: 僵硬 時間: 2025-3-22 02:30 作者: 提名 時間: 2025-3-22 05:18
Lexical Complexity Controlled Sentence Generation for?Language Learningodels. To facilitate the research, we develop two datasets in English and Chinese respectively, on which extensive experiments are conducted. Experimental results show that our approach provides more precise control over lexical complexity, as well as better fluency and diversity.作者: investigate 時間: 2025-3-22 11:34
Improving Zero-Shot Cross-Lingual Dialogue State Tracking via?Contrastive Learnings into a more refined language-invariant space. In addition, CLCL-DST uses a significance-based keyword extraction approach to select task-related words to build the bilingual dictionary for better cross-lingual positive examples. Experiment results on Multilingual WoZ 2.0 and parallel MultiWoZ 2.1 作者: 芭蕾舞女演員 時間: 2025-3-22 14:09 作者: 芭蕾舞女演員 時間: 2025-3-22 19:33 作者: crockery 時間: 2025-3-22 21:27 作者: enlist 時間: 2025-3-23 03:58 作者: 彎腰 時間: 2025-3-23 09:08 作者: 誓言 時間: 2025-3-23 10:48 作者: 太空 時間: 2025-3-23 16:56
E. J. Lous,M. Huber,R. A. Isaacson,G. Feherodels. To facilitate the research, we develop two datasets in English and Chinese respectively, on which extensive experiments are conducted. Experimental results show that our approach provides more precise control over lexical complexity, as well as better fluency and diversity.作者: Headstrong 時間: 2025-3-23 19:57 作者: 顯微鏡 時間: 2025-3-23 23:51
https://doi.org/10.1007/3-540-11699-0sk. Unlike prior RL-based RE approaches that usually fit value functions or compute policy gradients, TERL only outputs the best actions by utilizing a masked Transformer. Experimental results show that the proposed TERL framework can improve many state-of-the-art RL-based RE methods.作者: ASSET 時間: 2025-3-24 04:20 作者: ATOPY 時間: 2025-3-24 10:35
Polymerization with Formaldehyde,e Engineering, which contains a wealth of domain-specific knowledge. The experimental results on SSUIE-RE demonstrate the effectiveness of our method, achieving a 1.4% absolute improvement in relation F1 score over previous best approach.作者: Ascendancy 時間: 2025-3-24 14:03 作者: Talkative 時間: 2025-3-24 15:57 作者: attenuate 時間: 2025-3-24 22:18 作者: A精確的 時間: 2025-3-25 01:34
Overcoming Language Priors with?Counterfactual Inference for?Visual Question Answering efforts, causal inference is regarded as a promising direction to mitigate language bias by weakening the direct causal effect of questions on answers. In this paper, we follow the same direction and attack the issue of language priors by incorporating counterfactual data. Moreover, we propose a tw作者: sphincter 時間: 2025-3-25 07:06 作者: 難取悅 時間: 2025-3-25 09:26
Unsupervised Style Transfer in?News Headlines via?Discrete Style Spaceg data is one of the main problems in this field. In this work, we design a .iscrete style space for unsupervised .eadline .tyle .ransfer, short for .. This model decomposes the style-dependent text generation into content-feature extraction and style modelling. Then, generation decoder receives inp作者: 碎石頭 時間: 2025-3-25 11:47 作者: locus-ceruleus 時間: 2025-3-25 18:43
Improving Zero-Shot Cross-Lingual Dialogue State Tracking via?Contrastive Learnings is expensive. Existing models address this issue by code-switched data augmentation or intermediate fine-tuning of multilingual pre-trained models. However, these models can only perform implicit alignment across languages. In this paper, we propose a novel model named .ontrastive .earning for .ro作者: CHANT 時間: 2025-3-25 22:52 作者: 擁護 時間: 2025-3-26 02:10
A Distantly-Supervised Relation Extraction Method Based on?Selective Gate and?Noise Correctionh results are applied to various fields. To address the problem that current distantly supervised relation extraction (DSRE) methods based on large-scale corpus annotation generate a large amount of noisy data, a DSRE method that incorporates selective gate and noise correction framework is proposed作者: Crepitus 時間: 2025-3-26 04:18
Improving Cascade Decoding with?Syntax-Aware Aggregator and?Contrastive Learning for?Event Extractiontial benefits of the syntactic structure of sentences. In this paper, we improve cascade decoding with a novel module and a self-supervised task. Specifically, we propose a syntax-aware aggregator module to model the syntax of a sentence based on cascade decoding framework such that it captures eve作者: Entirety 時間: 2025-3-26 09:29 作者: 金哥占卜者 時間: 2025-3-26 13:13 作者: incontinence 時間: 2025-3-26 20:44
Self Question-Answering: Aspect Sentiment Triplet Extraction via?a?Multi-MRC Framework Based on?Rethed opinion terms that explain the underlying cause of the sentiment. Some recent studies fail to capture the strong interdependence between ATE and OTE, while others fail to effectively introduce the relationship between aspects and opinions into sentiment classification tasks. To solve these proble作者: 逢迎白雪 時間: 2025-3-27 00:50
Enhancing Ontology Knowledge for?Domain-Specific Joint Entity and?Relation Extractionetween general-domain text used for pre-training and domain-specific text, these methods encounter semantic redundancy and domain semantics insufficiency when it comes to domain-specific tasks. To mitigate this issue, we propose a low-cost and effective knowledge-enhanced method to facilitate domain作者: indubitable 時間: 2025-3-27 04:31 作者: 鴿子 時間: 2025-3-27 08:04
Native Bridging in React Native,Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thus achieving good performance. At the same time, the latter provides a clear reasoning lo作者: evasive 時間: 2025-3-27 12:47
The Ecosystem: Extending React Native,ditions. CQA is crucial for domains that require the provision of personalized advice or making context-dependent analyses, such as legal consulting and medical diagnosis. However, existing CQA models struggle with generating multiple conditional answers due to two main challenges: (1) the lack of s作者: atopic 時間: 2025-3-27 16:28
React Native for iOS Development efforts, causal inference is regarded as a promising direction to mitigate language bias by weakening the direct causal effect of questions on answers. In this paper, we follow the same direction and attack the issue of language priors by incorporating counterfactual data. Moreover, we propose a tw作者: maculated 時間: 2025-3-27 19:01
Flux: Solving Problems Differently,, supporting sentence prediction, and answer span extraction. In this work, we present the first application of label smoothing to the MHQA task, aiming to enhance generalization capabilities in MHQA systems while mitigating overfitting of answer spans and reasoning paths in the training set. We int作者: 冒號 時間: 2025-3-28 01:05
Starter React Project and Friends,g data is one of the main problems in this field. In this work, we design a .iscrete style space for unsupervised .eadline .tyle .ransfer, short for .. This model decomposes the style-dependent text generation into content-feature extraction and style modelling. Then, generation decoder receives inp作者: nettle 時間: 2025-3-28 03:42
E. J. Lous,M. Huber,R. A. Isaacson,G. Feherical complexity controlled sentence generation, which requires precise control over the lexical complexity in the keywords to examples generation and better fluency and semantic consistency. The challenge of this task is to generate fluent sentences only using words of given complexity levels. We pr作者: Respond 時間: 2025-3-28 06:18 作者: 去世 時間: 2025-3-28 10:27 作者: uncertain 時間: 2025-3-28 17:27 作者: Heretical 時間: 2025-3-28 21:23
S. T. Arnold,J. H. Hendricks,K. H. Bowenntial benefits of the syntactic structure of sentences. In this paper, we improve cascade decoding with a novel module and a self-supervised task. Specifically, we propose a syntax-aware aggregator module to model the syntax of a sentence based on cascade decoding framework such that it captures eve作者: 有偏見 時間: 2025-3-29 00:35
https://doi.org/10.1007/3-540-11699-0ledge graph construction and completion. Reinforcement Learning (RL) has been widely used for RE task and achieved SOTA results, which are mainly designed with rewards to choose the optimal actions during the training procedure, to improve RE’s performance, especially for low-resource conditions. Re作者: GEON 時間: 2025-3-29 06:20 作者: 消極詞匯 時間: 2025-3-29 11:12
The Plenum Chemical Engineering Seriesed opinion terms that explain the underlying cause of the sentiment. Some recent studies fail to capture the strong interdependence between ATE and OTE, while others fail to effectively introduce the relationship between aspects and opinions into sentiment classification tasks. To solve these proble作者: Exhilarate 時間: 2025-3-29 14:27 作者: thrombus 時間: 2025-3-29 15:42 作者: 畫布 時間: 2025-3-29 20:18 作者: DECRY 時間: 2025-3-30 01:55 作者: arabesque 時間: 2025-3-30 07:18
978-981-99-6206-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: SSRIS 時間: 2025-3-30 11:07
S. T. Arnold,J. H. Hendricks,K. H. Bowent event types, which could further boost the performance of event extraction. Experimental results on two widely used event extraction datasets demonstrate that our method could improve the original cascade decoding framework by up to 2.2 percentage points of F1 score and outperform a number of competitive baseline methods.作者: MOTIF 時間: 2025-3-30 14:19
Improving Cascade Decoding with?Syntax-Aware Aggregator and?Contrastive Learning for?Event Extractiot event types, which could further boost the performance of event extraction. Experimental results on two widely used event extraction datasets demonstrate that our method could improve the original cascade decoding framework by up to 2.2 percentage points of F1 score and outperform a number of competitive baseline methods.作者: 尊嚴 時間: 2025-3-30 20:22
0302-9743 nguage Resource and Evaluation, Pre-trained Language Models, Social Computing and Sentiment Analysis, NLP Applications..?978-981-99-6206-8978-981-99-6207-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Root494 時間: 2025-3-30 23:52
