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检索条件"任意字段=23rd Conference on Computational Natural Language Learning, CoNLL 2019"

共 100 条记录，以下是1-10 订阅

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conll 2019 - 23rd conference on computational natural language learning, Proceedings of the conference

CoNLL 2019 - 23rd Conference on Computational Natural Langua...

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23rd conference on computational natural language learning, conll 2019

ISBN: (纸本)9781950737727

The proceedings contain 97 papers. The topics discussed include: analyzing neural language models: contextual decomposition reveals default reasoning in number and gender assignment;deconstructing supertagging into multi-task sequence prediction;investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation;large-scale representation learning from visually grounded untranscribed speech;using priming to uncover the organization of syntactic representations in neural language models;compositional generalization in image captioning;cross-lingual word embeddings and the structure of the human bilingual lexicon;improving natural language understanding by reverse mapping bytepair encoding;and diversify your datasets: analyzing generalization via controlled variance in adversarial datasets.

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conll 2019 - SIGNLL conference on computational natural language learning, Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 conference on natural language learning

CoNLL 2019 - SIGNLL Conference on Computational Natural Lang...

引用

2019 Shared Task on Cross-Framework Meaning Representation Parsing, MRP 2019 at the 23rd conference for computational language learning, conll 2019

ISBN: (纸本)9781950737604

The proceedings contain 16 papers. The topics discussed include: SJTU-NICT at MRP2019: multi-task learning for end-to-end uniform semantic graph parsing;the ERG at MRP 2019: radically compositional semantic dependencies;Shanghaitech at MRP 2019: sequence-to-graph transduction with second-order edge inference for cross-framework meaning representation parsing;saarland at MRP 2019: compositional parsing across all graphbanks;HIT-SCIR AT MRP 2019: a unified pipeline for meaning representation parsing via efficient training and effective encoding;JBNU at MRP 2019: multi-level biaffine attention for semantic dependency parsing;and Hitachi at MRP 2019: unified encoder-to-biaffine network for cross-framework meaning representation parsing.

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Overview of CCL24-Eval Task 4: The Fourth Chinese Abstract Meaning Representation Parsing Evaluation 23

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23rd Chinese National conference on computational Linguistics, CCL 2024

作者： Xu, Zhixing Zhang, Yixuan Li, Bin Zhou, Junsheng Qu, Weiguang School of Chinese Language and Literature Nanjing Normal University China Center for Language Big Data and Computational Humanities Nanjing Normal University China School of Computer and Electronic Information Nanjing Normal University China

ISBN: (纸本)9780000000002

Meaning Representation has become a key research area in sentence-level semantic parsing within natural language processing. Substantial progress has been achieved in various NLP tasks using AMR. This paper presents the fourth Chinese Abstract Meaning Representation parsing evaluation, held during the technical evaluation task workshop at CCL 2024. The evaluation also introduced a new test set comprising Ancient Chinese sentences. Results indicated decent performance, with the top team achieving an F1 of 0.8382 in the open modality, surpassing the previous record at conll 2020 by 3.30 percentage points under the MRP metric. However, current large language models perform poorly in AMR parsing of Ancient Chinese, highlighting the need for effective training strategies. The complex syntax and semantics of Ancient Chinese pose significant challenges. Additionally, optimizing transfer learning techniques to better apply knowledge from Chinese Mandarin to Ancient Chinese parsing is crucial. Only through continuous innovation and collaboration can significant advancements in both Ancient Chinese and Chinese Mandarin AMR parsing be achieved. © 2024 China National conference on computational Linguistics.

关键词： Semantics

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Overview of CCL24-Eval Task 5: Classical Chinese Historical Event Detection Evaluation 23

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23rd Chinese National conference on computational Linguistics, CCL 2024

作者： Feng, Zhenbing Li, Wei Shao, Yanqiu Information Science School Beijing Language and Culture University China Language Resources Monitoring and Research Center 15 Xueyuan Road HaiDian District Beijing100083 China

ISBN: (纸本)9780000000002

Event detection involves identifying and extracting event information from natural language texts. The complex syntax and semantics of Classical Chinese, coupled with its limited usage, pose significant challenges for information extraction tasks on classical Chinese texts. At the 23rd China National conference on computational Linguistics (CCL 2024), we launched an evaluation task focused on the extraction of historical events from Classical Chinese. We used our constructed Classical Chinese Historical Event Logical Schema to identify event triggers and classify event types. The evaluation utilized the Classical Chinese Historical Event Detection Dataset (CHED), annotated from The Twenty-Four Histories corpus, with the aim of enhancing event extraction technologies and advancing the digital study of classical Chinese historical texts. The evaluation included two subtasks and attracted 28 teams, with 15 teams submitting valid results. In the subtask of trigger identification, the best-performing system achieved an Exact match score of 63.6%. In the subtasks of coarse-grained and fine-grained event type classification, the top systems achieved F1-scores of 84.5% and 81.4%, respectively. © 2024 China National conference on computational Linguistics.

关键词： Semantics

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Prior Constraints-based Reward Model Training for Aligning Large language Models 23

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23rd Chinese National conference on computational Linguistics, CCL 2024

作者： Zhou, Hang Wang, Chenglong Hu, Yimin Xiao, Tong Zhang, Chunliang Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China

ISBN: (纸本)9780000000002

Reinforcement learning with human feedback for aligning large language models (LLMs) trains a reward model typically using ranking loss with comparison pairs. However, the training procedure suffers from an inherent problem: the uncontrolled scaling of reward scores during reinforcement learning due to the lack of constraints while training the reward model. This paper proposes a Prior Constraints-based Reward Model (PCRM) training method to mitigate this problem. PCRM incorporates prior constraints—specifically, length ratio and cosine similarity between outputs of each comparison pair—during reward model training to regulate optimization magnitude and control score margins. We comprehensively evaluate PCRM by examining its rank correlation with human preferences and its effectiveness in aligning LLMs via RL. Experimental results demonstrate that PCRM significantly improves alignment performance by effectively constraining reward score scaling. As another bonus, our method is easily integrated into arbitrary rank-based alignment methods, such as direct preference optimization, and can yield consistent improvement. The code is available at https://***/wangclnlp/DeepSpeed-Chat-Extension/tree/PCRM. © 2024 China National conference on computational Linguistics.

关键词： computational linguistics

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LLM-based Sign language Production 23

LLM-based Sign Language Production

引用

23rd IEEE International conference on Machine learning and Applications, ICMLA 2024

作者： Silveira, Wellington Mendonça, Luca De Bem, Rodrigo Federal University of Rio Grande Center of Computational Sciences Rio Grande Brazil

ISBN: (纸本)9798350374889

Sign language is an effective means of communication for individuals with different degrees of hearing impairment. Assistive technologies are a significant ally in the social inclusion of people from these groups. Recent advancements in natural language models and high-definition image generation emerged as a novel, non-intrusive approach for tasks related to sign language recognition (SLR), translation (SLT), and production (SLP). This work introduces a novel sign language production approach based on Large language Models (LLMs) capable of translating text to signs, which are then synthesized into sequences of images. Our method is tested with two LLMs, the LLaMA-7B-hf (quantized) and the Vicuna-7B. Experiments performed on two sign language datasets, the RWTH-PHOENIX-Weather, and the SynLibras-Pose, have shown promising results for this cross-modal application of LLMs. © 2024 IEEE.

关键词： Assistive technology

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Overview of CCL24-Eval Task 10: Translation Quality Evaluation of Sign language Avatar 23

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23rd Chinese National conference on computational Linguistics, CCL 2024

作者： Zhao, Yuan Zhang, Ruiquan Yao, Dengfeng Chen, Yidong Beijing Key Laboratory of Information Service Engineering Beijing Union University China School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Lab of Computational Linguistics School of Humanities Tsinghua University China

ISBN: (纸本)9780000000002

Sign language Avatar technology aims to create virtual agents capable of communicating with deaf individuals through sign language, similar to the text dialogue agent ChatGPT but focusing on sign language communication. Challenges in sign language production include limited dataset sizes, information loss due to reliance on intermediate representations, and insufficient realism in generated actions. In this event, we particularly focus on the ability of the Sign language Avatar to translate spoken language text into sign language that is easily understood by deaf individuals. As the first sign language avatar event held by the China National conference on computational Linguistics(CCL), this event attracted wide attention from both industry and academia, with 14 teams registering and 10 of them submitting their system interfaces on time. We provided a dataset consisting of 1074 text-video parallel sentence pairs for training, and the evaluation team comprised proficient Chinese sign language users and professional sign language translators. The scoring method employed a comprehensive evaluation based on multiple metrics, focusing primarily on sign language grammar accuracy, naturalness, readability, and cultural adaptability. The final scores were determined by considering performance across these four aspects. The final scores, taking into account these four aspects, showed that four teams demonstrated good readability, with Vivo Mobile Communication Co., Ltd. ranking first with a score of 3.513 (out of a full score of 5), leading the baseline model by 1.394 points. According to the analysis of the results, most teams used the traditional method of converting text into Gloss sequences before generating sign language. Additionally, some teams experimented with emerging methods, including gloss-free end-to-end training and Large language Model(LLMs) prompt learning, which also achieved promising results. We anticipate that this event will promote the development of s

关键词： Chatbots

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Balancing and Contrasting Biased Samples for Debiased Visual Question Answering 23

Balancing and Contrasting Biased Samples for Debiased Visual...

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23rd IEEE International conference on Data Mining (IEEE ICDM)

作者： Cao, Runlin Li, Zhixin Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

ISBN: (纸本)9798350307887

The goal of Visual Question Answering (VQA) is to test the reasoning ability of an intelligent agent by evaluating visual and textual information. However, recent studies suggest that many VQA models may only capture the correlation between questions and answers in the dataset rather than demonstrating true reasoning ability. To address this issue, we propose a new training approach called Balancing and Contrasting Biased Samples for Debiased VQA (BC-VQA) to build a robust VQA model. In our approach, we first generate two types of negative samples to balance the biased data and use self-supervised auxiliary tasks to help the base VQA model overcome language priors. Our method does not require any additional annotations. We then filter out biased training samples, construct positive samples by eliminating spurious correlations in biased samples, and perform auxiliary training through contrastive learning. Our approach is straightforward to implement and compatible with various VQA backbones. The experimental results demonstrate that BC-VQA achieves higher accuracy on VQA-CP v2 compared to the current state-of-the-art approaches.

关键词： Robust VQA language Priors Visual Question Answering Contrastive learning Self-supervised learning

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Chinese Grammatical Error Correction via Large language Model Guided Optimization Training 23

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23rd Chinese National conference on computational Linguistics, CCL 2024

作者： Liu, Xiao Li, Ying Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9780000000002

Pre-trained language model-based methods for Chinese Grammatical Error Correction (CGEC) are categorized into Seq2Seq and Seq2Edit types. However, both Seq2Seq and Seq2Edit models depend on high-quality training data significantly. Considering the strong generation and inference ability of large language models (LLMs), we propose a large language model-guided optimization training method to exploit LLMs to extract error knowledge to optimize the traditional CGEC model training process. On the one hand, we use error types and confusion sets as extra knowledge to guide LLMs to generate diverse pseudo data, thus extending the error distribution of our training data. On the other hand, LLMs are utilized to infer the predicted results from our CGEC models and obtain the re-training data, thus iteratively optimizing our pre-trained CGEC models. Experiments on two benchmark datasets show that our LLMs-guided optimization method with small-scale training data can achieve comparable results with baseline models with large-scale training data. Detailed comparison experiments demonstrate that both the early deviser pseudo data and the later re-training data are extremely useful for traditional CGEC model optimization training, and can benefit from each other. We will release our code and prompts at https://***/SakuraAcedia/llm-cgec-got to facilitate future work. © 2024 China National conference on computational Linguistics.

关键词： Digital elevation model

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OPEx: A Large language Model-Powered Framework for Embodied Instruction Following 23

OPEx: A Large Language Model-Powered Framework for Embodied ...

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23rd International conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Shi, Haochen Sun, Zhiyuan Yuan, Xingdi Côté, Marc-Alexandre Liu, Bang Université de Montréal Mila Montréal Canada Microsoft Research Montréal Canada

Embodied Instruction Following (EIF) is crucial for understanding natural language in a practical context, requiring agents to follow verbal instructions for complex tasks. Traditionally, EIF relies heavily on expert annotations for learning, which are costly and sometimes unattainable. Recent research shows Large language Models (LLMs) can use their reasoning ability to help in EIF with minimal examples, but applying LLMs directly faces issues like hallucinations and partially observable environment. To bridge the gap, we introduce OPEx, a new LLM-based method for EIF that needs far less specific data. OPEx uses three LLMs for different roles: observing to gather environment data, planning by breaking down instructions, and executing tasks with learned skills. Our tests reveal OPEx significantly outperforms the FILM baseline, with 90% less training data for planning tasks and achieving up to 38% performance gain when FILM is trained on identical data. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： computational linguistics

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