
汀丶
V1
2023/02/06阅读:88主题:默认主题
2023计算机领域顶会(A类)以及ACL 2023自然语言处理(NLP)研究子方向领域汇总
2023年的计算语言学协会年会(ACL 2023)共包含26个领域,代表着当前前计算语言学和自然语言处理研究的不同方面。每个领域都有一组相关联的关键字来描述其潜在的子领域, 这些子领域并非排他性的,它们只描述了最受关注的子领域,并希望能够对该领域包含的相关类型的工作提供一些更好的想法。
1.计算机领域顶会(A类)
会议简称 | 主要领域 | 会议全称 | 官网 | 截稿时间 | 会议时间 |
---|---|---|---|---|---|
CVPR2023 | 计算机视觉 | The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 | https://cvpr2023.thecvf.com/ | 2022.11.11 | 2023.6.18 |
ICCV2023 | 计算机视觉 | IEEE International Conference on Computer Vision | https://iccv2023.thecvf.com/ | 2023.3.8 | 2023.9.30 |
ECCV2022 | 计算机视觉 | European Conference on Computer Vision | https://eccv2022.ecva.net/ | ------- | 2022.10.23 |
AAAI2023 | 人工智能 | National Conference of the American Association for Artificial Intelligence | https://aaai-23.aaai.org/ | 2022.8.8 | 2023.2.7 |
IJCAI 2023 | 人工智能 | National Conference of the American Association for Artificial Intelligence | https://ijcai-22.org/# | 2022.8.8 | 2023.2.7 |
NIPS2023 | 机器学习 | International Joint Conference on Artificial Intelligence | https://neurips.cc/Conferences/2022 | 2023.01 | 2023.07 |
ICML 2023 | 机器学习 | International Conference on Machine Learning | https://icml.cc/ | 2023.01 | 2023.06.24 |
ICLR 2023 | 机器学习 | International Conference on Learning Representations | https://iclr.cc/Conferences/2023 | 2022.09.21 | 2023.05.01 |
ICSE 2023 | 软件工程 | International Conference on Software Engineering | https://conf.researchr.org/home/icse-2023 | 2022.09.01 | 2023.05.14 |
SIGKDD 2023 | 数据挖掘 | ACM International Conference on Knowledge Discovery and Data Mining | https://kdd.org/kdd2022/index.html | 2023.02 | 2023.08 |
SIGIR 2023 | 数据挖掘 | ACM International Conference on Research and Development in Information Retrieval | https://sigir.org/sigir2022/ | 2023.01 | 2023.07 |
ACL 2023 | 计算语言 | Association of Computational Linguistics | https://www.2022.aclweb.org/ | 2022.11 | 2023.05 |
ACM MM 2023 | 多媒体 | ACM International Conference on Multimedia | https://2023.acmmmsys.org/participation/important-dates/ | 2022.11.18 | 2023.6.7 |
WWW2023 | 网络应用 | International World Wide Web Conference | https://www2023.thewebconf.org/ | 2022.10.6 | 2023.05.01 |
SIGGRAPH 2023 | 图形学 | ACM SIG International Conference on Computer Graphics and Interactive Techniques | https://s2022.siggraph.org/ | 2023.01 | 2023.08 |
CHI 2023 | 人机交互 | ACM Conference on Human Factors in Computing Systems | https://chi2023.acm.org/ | 2022.09.08 | 2023.04.23 |
CSCW 2023 | 人机交互 | ACM Conference on Computer Supported Cooperative Work and Social Computing | https://cscw.acm.org/2023/ | 2023.01.15 | 2023.10.13 |
CCS 2023 | 信息安全 | ACM Conference on Computer and Communications Security | https://www.sigsac.org/ccs/CCS2022/ | 2023.01 | 2023.11 |
VLDB 2023 | 数据管理 | International Conference on Very Large Data Bases | https://www.vldb.org/2023/?submission-guidelines | 2023.03.01 | 2023.08.28 |
STOC 2023 | 计算机理论 | ACM Symposium on the Theory of Computing | http://acm-stoc.org/stoc2022/ | 2022.11 | 2023.06 |
2.ACL 2023自然语言处理(NLP)研究子方向领域汇总
(一)计算社会科学和文化分析 (Computational Social Science and Cultural Analytics)
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人类行为分析 (Human behavior analysis) -
态度检测 (Stance detection) -
框架检测和分析 (Frame detection and analysis) -
仇恨言论检测 (Hate speech detection) -
错误信息检测和分析 (Misinformation detection and analysis) -
人口心理画像预测 (psycho-demographic trait prediction) -
情绪检测和分析 (emotion detection and analysis) -
表情符号预测和分析 (emoji prediction and analysis) -
语言和文化偏见分析 (language/cultural bias analysis) -
人机交互 (human-computer interaction) -
社会语言学 (sociolinguistics) -
用于社会分析的自然语言处理工具 (NLP tools for social analysis) -
新闻和社交媒体的定量分析 (quantiative analyses of news and/or social media)
(二)对话和交互系统 (Dialogue and Interactive Systems)
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口语对话系统 (Spoken dialogue systems) -
评价指标 (Evaluation and metrics) -
任务型 (Task-oriented) -
人工介入 (Human-in-a-loop) -
偏见和毒性 (Bias/toxity) -
事实性 (Factuality) -
检索 (Retrieval) -
知识增强 (Knowledge augmented) -
常识推理 (Commonsense reasoning) -
互动讲故事 (Interactive storytelling) -
具象代理人 (Embodied agents) -
应用 (Applications) -
多模态对话系统 (Multi-modal dialogue systems) -
知识驱动对话 (Grounded dialog) -
多语言和低资源 (Multilingual / low-resource) -
对话状态追踪 (Dialogue state tracking) -
对话建模 (Conversational modeling)
(三)话语和语用学 (Discourse and Pragmatics)
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回指消解 (Anaphora resolution) -
共指消解 (Coreference resolution) -
桥接消解 (Bridging resolution) -
连贯 (Coherence) -
一致 (Cohesion) -
话语关系 (Discourse relations) -
话语分析 (Discourse parsing) -
对话 (Dialogue) -
会话 (Conversation) -
话语和多语性 (Dialugue and multilinguality) -
观点挖掘 (Argument mining) -
交际 (Communication)
(四)自然语言处理和伦理 (Ethics and NLP)
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数据伦理 (Data ethics) -
模型偏见和公正性评价 (Model bias/fairness evaluation) -
减少模型的偏见和不公平性 (Model bias/unfairness mitigation) -
自然语言处理中的人类因素 (Human factors in NLP) -
参与式和基于社群的自然语言处理 (Participatory/community-based NLP) -
自然语言处理应用中的道德考虑 (Ethical considerations in NLP) -
透明性 (Transparency) -
政策和治理 (Policy and governance) -
观点和批评 (Reflections and critiques)
(五)语言生成 (Generation)
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人工评价 (Human evaluation) -
自动评价 (Automatic evaluation) -
多语言 (Multilingualism) -
高效模型 (Efficient models) -
少样本生成 (Few-shot generation) -
分析 (Analysis) -
领域适应 (Domain adaptation) -
数据到文本生成 (Data-to-text generation) -
文本到文博生成 (Text-to-text generation) -
推断方法 (Inference methods) -
模型结构 (Model architectures) -
检索增强生成 (Retrieval-augmented generation) -
交互和合作生成 (Interactive and collaborative generation)
(六)信息抽取 (Information Extraction)
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命名实体识别和关系抽取 (Named entity recognition and relation extraction) -
事件抽取 (Event extraction) -
开放信息抽取 (Open information extraction) -
知识库构建 (Knowledge base construction) -
实体连接和消歧 (Entity linking and disambiguation) -
文档级抽取 (Document-level extraction) -
多语言抽取 (Multilingual extraction) -
小样本和零样本抽取 (Zero-/few-shot extraction)
(七)信息检索和文本挖掘 (Information Retrieval and Text Mining)
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段落检索 (Passage retrieval) -
密集检索 (Dense retrieval) -
文档表征 (Document representation) -
哈希 (Hashing) -
重排序 (Re-ranking) -
预训练 (Pre-training) -
对比学习 (Constrastive learning)
(八)自然语言处理模型的可解释性与分析 (Interpretability and Analysis of Models in NLP)
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对抗性攻击/例子/训练 (Adversarial attacks/examples/training) -
校正和不确定性 (Calibration/uncertainty) -
反事实和对比解释 (Counterfactual/contrastive explanations) -
数据影响 (Data influence) -
数据瑕疵 (Data shortcuts/artifacts) -
解释的忠诚度 (Explantion faithfulness) -
特征归因 (Feature attribution) -
自由文本和自然语言解释 (Free-text/natural language explanation) -
样本硬度 (Hardness of samples) -
结构和概念解释 (Hierarchical & concept explanations) -
以人为主体的应用评估 (Human-subject application-grounded evaluations) -
知识追溯、发现和推导 (Knowledge tracing/discovering/inducing) -
探究 (Probing) -
稳健性 (Robustness) -
话题建模 (Topic modeling)
(九)视觉、机器人等领域的语言基础 (Language Grounding to Vision, Robotics and Beyond)
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视觉语言导航 (Visual Language Navigation) -
跨模态预训练 (Cross-modal pretraining) -
图像文本匹配 (Image text macthing) -
跨模态内容生成 (Cross-modal content generation) -
视觉问答 (Visual question answering) -
跨模态应用 (Cross-modal application) -
跨模态信息抽取 (Cross-modal information extraction) -
跨模态机器翻译 (Cross-modal machine translation)
(十)大模型(Large Language Models)
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预训练 (Pre-training) -
提示 (Prompting) -
规模化 (Scaling) -
稀疏模型 (Sparse models) -
检索增强模型 (Retrieval-augmented models) -
伦理 (Ethics) -
可解释性和分析 (Interpretability/Analysis) -
连续学习 (Continual learning) -
安全和隐私 (Security and privacy) -
应用 (Applications) -
稳健性 (Robustness) -
微调 (Fine-tuning)
(十一)语言多样性 (Language Diversity)
-
少资源语言 (Less-resource languages) -
濒危语言 (Endangered languages) -
土著语言 (Indigenous languages) -
少数民族语言 (Minoritized languages) -
语言记录 (Language documentation) -
少资源语言的资源 (Resources for less-resourced languages) -
软件和工具 (Software and tools)
(十二)语言学理论、认知建模和心理语言学 (Linguistic Theories, Cognitive Modeling and Psycholinguistics)
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语言学理论 (Linguistic theories) -
认知建模 (Cognitive modeling) -
计算心理语言学 (Computational pyscholinguistics)
(十三)自然语言处理中的机器学习 (Machine Learning for NLP)
-
基于图的方法 (Graph-based methods) -
知识增强的方法 (Knowledge-augmented methods) -
多任务学习 (Multi-task learning) -
自监督学习 (Self-supervised learning) -
对比学习 (Contrastive learning) -
生成模型 (Generation model) -
数据增强 (Data augmentation) -
词嵌入 (Word embedding) -
结构化预测 (Structured prediction) -
迁移学习和领域适应 (Transfer learning / domain adaptation) -
表征学习 (Representation learning) -
泛化 (Generalization) -
模型压缩方法 (Model compression methods) -
参数高效的微调方法 (Parameter-efficient finetuning) -
少样本学习 (Few-shot learning) -
强化学习 (Reinforcement learning) -
优化方法 (Optimization methods) -
连续学习 (Continual learning) -
对抗学习 (Adversarial training) -
元学习 (Meta learning) -
因果关系 (Causality) -
图模型 (Graphical models) -
人参与的学习和主动学习 (Human-in-a-loop / Active learning)
(十四)机器翻译 (Machine Translation)
-
自动评价 (Automatic evaluation) -
偏见 (Biases) -
领域适应 (Domain adaptation) -
机器翻译的高效推理方法 (Efficient inference for MT) -
高效机器翻译训练 (Efficient MT training) -
少样本和零样本机器翻译 (Few-/Zero-shot MT) -
人工评价 (Human evaluation) -
交互机器翻译 (Interactive MT) -
机器翻译部署和维护 (MT deployment and maintainence) -
机器翻译理论 (MT theory) -
建模 (Modeling) -
多语言机器翻译 (Multilingual MT) -
多模态 (Multimodality) -
机器翻译的线上运用 (Online adaptation for MT) -
并行解码和非自回归的机器翻译 (Parallel decoding/non-autoregressive MT) -
机器翻译预训练 (Pre-training for MT) -
规模化 (Scaling) -
语音翻译 (Speech translation) -
转码翻译 (Code-switching translation) -
词表学习 (Vocabulary learning)
(十五)多语言和跨语言自然语言处理 (Multilingualism and Cross-Lingual NLP)
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转码 (Code-switching) -
混合语言 (Mixed language) -
多语言 (Multilingualism) -
语言接触 (Language contact) -
语言变迁 (Language change) -
语言变体 (Language variation) -
跨语言迁移 (Cross-lingual transfer) -
多语言表征 (Multilingual representation) -
多语言预训练 (Multilingual pre-training) -
多语言基线 (Multilingual benchmark) -
多语言评价 (Multilingual evaluation) -
方言和语言变种 (Dialects and language varieties)
(十六)自然语言处理应用 (NLP Applications)
-
教育应用、语法纠错、文章打分 (Educational applications, GEC, essay scoring) -
仇恨言论检测 (Hate speech detection) -
多模态应用 (Multimodal applications) -
代码生成和理解 (Code generation and understanding) -
事实检测、谣言和错误信息检测 (Fact checking, rumour/misinformation detection) -
医疗应用、诊断自然语言处理 (Healthcare applications, clinical NLP) -
金融和商务自然语言处理 (Financial/business NLP) -
法律自然语言处理 (Legal NLP) -
数学自然语言处理 (Mathematical NLP) -
安全和隐私 (Security/privacy) -
历史自然语言处理 (Historical NLP) -
知识图谱 (Knowledge graph)
(十七)音系学、形态学和词语分割 (Phonology, Morphology and Word Segmentation)
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形态变化 (Morphological inflection) -
范式归纳 (Paradigm induction) -
形态学分割 (Morphological segementation) -
子词表征 (Subword representations) -
中文分割 (Chinese segmentation) -
词性还原 (Lemmatization) -
有限元形态学 (Finite-state morphology) -
形态学分析 (Morphological analysis) -
音系学 (Phonology) -
字素音素转换 (Grapheme-to-phoneme conversion) -
发音建模 (Pronunciation modeling)
(十八)问答 (Question Answering)
-
常识问答 (Commonsense QA) -
阅读理解 (Reading comprehension) -
逻辑推理 (Logic reasoning) -
多模态问答 (Multimodal QA) -
知识库问答 (Knowledge base QA) -
语义分析 (Semantic parsing) -
多跳问答 (Multihop QA) -
生物医学问答 (Biomedical QA) -
多语言问答 (Multilingual QA) -
可解释性 (Interpretability) -
泛化 (Generalization) -
推理 (Reasoning) -
对话问答 (Conversational QA) -
少样本问答 (Few-shot QA) -
数学问答 (Math QA) -
表格问答 (Table QA) -
开放域问答 (Open-domain QA) -
问题生成 (Question generation)
(十九)语言资源及评价 (Resources and Evaluation)
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语料库构建 (Corpus creation) -
基线构建 (Benchmarking) -
语言资源 (Language resources) -
多语言语料库 (Multilingual corpora) -
词表构建 (Lexicon creation) -
语言资源的自动构建与评价 (Automatic creation and evaluation of language resources) -
自然语言处理数据集 (NLP datasets) -
数据集自动评价 (Automatic evaluation of datasets) -
评价方法 (Evaluation methodologies) -
低资源语言数据集 (Datasets for low resource languages) -
测量指标 (Metrics) -
复现性 (Reproducibility) -
用于评价的统计检验 (Statistical testing for evaluation)
(二十)语义学:词汇层面 (Semantics: Lexical)
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一词多义 (Polysemy) -
词汇关系 (Lexical relationships) -
文本蕴含 (Textual entailment) -
语义合成性 (Compositionality) -
多词表达 (Multi-word expressions) -
同义转换 (Paraphrasing) -
隐喻 (Metaphor) -
词汇语义变迁 (Lexical semantic change) -
词嵌入 (Word embeddings) -
认知 (Cognition) -
词汇资源 (Lexical resources) -
情感分析 (Sentiment analysis) -
多语性 (Multilinguality) -
可解释性 (Interpretability) -
探索性研究 (Probing)
(二十一)语义学:句级语义、文本推断和其他领域 (Semantics: Sentence-Level Semantics, Textual Inference and Other Areas)
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同义句识别 (Paraphrase recognition) -
文本蕴含 (Textual entailment) -
自然语言推理 (Natural language inference) -
逻辑推理 (Reasoning) -
文本语义相似性 (Semantic textual similarity) -
短语和句子嵌入 (Phrase/sentence embedding) -
同义句生成 (Paraphrase generation) -
文本简化 (Text simiplification) -
词和短语对齐 (Word/phrase alignment)
(二十二)情感分析、文本风格分析和论点挖掘 (Sentiment Analysis, Stylistic Analysis and Argument Mining)
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论点挖掘 (Argument mining) -
观点检测 (Stance detection) -
论点质量评价 (Argument quality assessment) -
修辞和框架 (Rhetoric and framing) -
论证方案和推理 (Argument schemes and reasoning) -
论点生成 (Argument generation) -
风格分析 (Style analysis) -
风格生成 (Style generation) -
应用 (Applications)
(二十三)语音和多模态 (Speech and Multimodality)
-
自动语音识别 (Automatic speech recognition) -
口语语言理解 (Spoken language understanding) -
口语翻译 (Spoken language translation) -
口语语言基础 (Spoken language grounding) -
语音和视觉 (Speech and vision) -
口语查询问答 (QA via spoken queries) -
口语对话 (Spoken dialog) -
视频处理 (Video processing) -
语音基础 (Speech technologies) -
多模态 (Multimodality)
(二十四)文摘 (Summarization)
-
抽取文摘 (Extractive summarization) -
摘要文摘 (Abstractive summarization) -
多模态文摘 (Multimodal summarization) -
多语言文摘 (Multilingual summarization) -
对话文摘 (Conversational summarization) -
面向查询的文摘 (Query-focused summarization) -
多文档文摘 (Multi-document summarization) -
长格式文摘 (Long-form summarization) -
句子压缩 (Sentence compression) -
少样本文摘 (Few-shot summarization) -
结构 (Architectures) -
评价 (Evaluation) -
事实性 (Factuality)
(二十五)句法学:标注、组块分析和句法分析 (Syntax: Tagging, Chunking and Parsing)
-
组块分析、浅层分析 (Chunking, shallow-parsing) -
词性标注 (Part-of-speech tagging) -
依存句法分析 (Dependency parsing) -
成分句法分析 (Constituency parsing) -
深层句法分析 (Deep syntax parsing) -
语义分析 (Semantic parsing) -
句法语义接口 (Syntax-semantic inferface) -
形态句法相关任务的标注和数据集 (Optimized annotations or data set for morpho-syntax related tasks) 句法分析算法 (Parsing algorithms) -
语法和基于知识的方法 (Grammar and knowledge-based approach) -
多任务方法 (Multi-task approaches) -
面向大型多语言的方法 (Massively multilingual oriented approaches) -
低资源语言词性标注、句法分析和相关任务 (Low-resource languages pos-tagging, parsing and related tasks) -
形态丰富语言的词性标注、句法分析和相关任务 (Morphologically-rich languages pos tagging, parsing and related tasks)
(二十六)主题领域:现实检测 (Theme Track: Reality Check)
-
因为错误的原因而正确 (Right for the wrong reasons) -
实际运用中的教训 (Lessons from deployment) -
(非)泛化能力 [(Non-)generalization] -
(非)复现能力 [(Non-)reproducibility)] -
评价 (Evaluation) -
方法 (Methodology) -
负面结果 (Negative results) -
人工智能噱头和期待 (AI hype and expectations) -
科学 vs 工程 (Science-vs-engineering) -
其他领域的结合 (Lessons from other fields)
作者介绍

汀丶
V1
将不定期更新关于NLP等领域相关知识,