Paper History (1)

LLM 페이지: https://ai-information.blogspot.com/2024/11/paper-history-llm.html

* 읽어볼것

  • search result diversification
    • Diversity by proportionality: an election-based approach to search result diversification, SIGIR 2012
  • MIMICS‐Duo: Offline & Online Evaluation of Search Clarification, SIGIR 2022 (Resource Track)
  • 읽을 논문 찾기
    • https://github.com/jxzhangjhu/Awesome-LLM-RAG
    • Large Language Models: A Survey
    • https://github.com/Hannibal046/Awesome-LLM?tab=readme-ov-file
    • https://github.com/dair-ai/ML-Papers-of-the-Week
    • https://www.promptingguide.ai/papers
  • 저자로 참여한 논문

1. Natural Language Generation

  1. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient, AAAI 2017 [포스팅]
  2. MaliGAN: Maximum-Likelihood Augmented Discrete Generative Adversarial Networks, Preprint 2018 [포스팅]
  3. MaskGAN: Better Text Generation via Filling in the ______, ICLR 2018 [포스팅]

2. Style Transfer

  1. Toward Controlled Generation of Text, ICML 2017 [포스팅]
  2. Style Transfer from Non-Parallel Text by Cross-Alignment, NIPS 2017 [포스팅]
  3. Style transfer in text: Exploration and evaluation, AAAI 2018 [포스팅]
  4. Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer, NAACL 2018 (2018. 04) [포스팅]
  5. Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation, ACL 2019 (2019. 05) [포스팅]
  6. A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer, IJCAI 2019, (2019. 05) [포스팅]
  7. Domain Adaptive Text Style Transfer, EMNLP 2019 [포스팅]
  8. Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer, EMNLP 2019 [포스팅]
  9. Mask and Infill: Applying Masked Language Model to Sentiment Transfer, IJCAI 2019 [포스팅]
  10. Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer, INLG 2020
  11. Adjustable Text Style Transfer with Dynamic Representative Style Feature, ARR 2022.01 [포스팅]
  12. Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models, ARR 2022.04
  13. SYNDEC: A Synthesize-then-Decode Approach for Arbitrary Textual Style Transfer via Large Language Models, ARR Review 2505
  14. Data Generation Disguised as Style-Transfer: The LLM Perspective, ARR Review 2505 

2.1 Multiple Style Transfer

  1. Multiple-Attribute Text Style Transfer, ICLR 2019 (2018. 11) [포스팅]
    1. Multiple-Attribute Text Rewriting
  2. Content preserving text generation with attribute controls, NIPS 2018 [포스팅]

3. Data-to-Text generation

3.1 E2E Challenge

  1. The E2E NLG Challenge: A Tale of Two Systems, INLG 2018 [포스팅]
  2. E2E NLG Challenge: Neural Models vs. Templates, INLG 2018 [포스팅]
  3. A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation, NAACL 2018 [포스팅]
  4. Learning Neural Templates for Text Generation, EMNLP 2018 [포스팅]
  5. End-to-End Content and Plan Selection for Data-to-Text Generation, INLG 2018 (2018. 10)
  6. Pragmatically Informative Text Generation, NAACL 2019 [포스팅]
  7. Designing a Symbolic Intermediate Representation for Neural Surface Realization, NAACL WS 2019 [포스팅]
  8. Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue, ACL 2019 [포스팅]
  9. Toward Improving Coherence and Diversity of Slogan Generation, 2021 NLE (review)
  10. Transforming Multi-Conditioned Generation from Meaning Representation, RANLP 2021

3.2 ToTTo Dataset

  1. ToTTo: A Controlled Table-To-Text Generation Dataset, EMNLP 2020 [포스팅]
  2. Text-to-Text Pre-Training for Data-to-Text Tasks, INLG 2020

3.3 WebNLG Challenge

  1. Auomatic Best: Melbourne (리포트)
  2. (Human Best, Grammer based) FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers, SEMVAL WS 2017 [리포트]

4. Dialogue

4.1 Generation

  1. Deep Reinforcement Learning for Dialogue Generation, EMNLP 2016 [포스팅] (Opensubtitles 데이터세트)
  2. Adversarial Learning for Neural Dialogue Generation, EMNLP 2017 [포스팅] (Opensubtitles 데이터세트)
  3. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders, ACL 2017 [포스팅] (Switchboard (SW) 데이터세트)
  4. DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation, ACL 2020 system demonstration [포스팅] (Reddit dataset, code) [HuggingFace]
  5. PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning, Preprint 2020 [포스팅]
  6. Domain Adaptive Dialog Generation via Meta Learning, ACL 2019 [포스팅]

4.2 Response Selection

  1. Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring, ICLR 2020 [포스팅]
  2. The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection, EMNLP 2020 [포스팅]
  3. MuTual: A Dataset for Multi-Turn Dialogue Reasoning, ACL 2020 [포스팅]
  4. Fine-grained Post-training for Improving Retrieval-based Dialogue Systems, NAACL 2021 [포스팅]
  5. Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues, AAAI 2021 [포스팅]
  6. An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model, EMNLP 2021
  7. Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations, ACL 2022 [포스팅]

4.2.1 데이터 관점

  1. Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems, EMNLP 2019 [포스팅]
  2. Dialogue Response Selection with Hierarchical Curriculum Learning, ACL 2021 [포스팅]
  3. Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation, Findings of ACL 2021 [포스팅]
  4. Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model, NAACL 2021 [포스팅]

4.2.2 Personalized Response Selection

  1. Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots, EMNLP 2019 [포스팅]
  2. Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems, CIKM 2020 (정리)
  3. Content Selection Network for Document-grounded Retrieval-based Chatbots, ECIR 2021 [포스팅]
  4. Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots, Findings of EMNLP 2020 [포스팅]
  5. Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots, SIGIR 2021 [포스팅]
  6. COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas, SIGIR 2022 [포스팅]
  7. Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection, COLING 2022 [포스팅]
  8. P5: Plug-and-Play Persona Prompting for Personalized Response Selection, EMNLP 2023

4.2.3 Ranking Metric

4.3 Response Generation

  1. CORAL: A Conversation-History Sensitive Loss Function for Effective Dialog Generation, SIGIR Review 2024 [포스팅]
  2. EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning, Cogsci 2024 Review
  3. DSCL: Dual-Semantic Contrastive Learning for Empathetic Response Generation, SIGIR 2024 Review

4.3.1 Personalized Response Generation

  1. Personalizing dialogue agents: I have a dog, do you have pets too?, ACL 2018 [포스팅] 
  2. TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents, NeurIPS 2018 CAI Workshop [포스팅]
  3. You Impress Me: Dialogue Generation via Mutual Persona Perception, ACL 2020 [포스팅] 
  4. Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation, ACL 2020 [포스팅]
  5. Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge, AAAI 2022 [포스팅]
  6. Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances, NAACL 2022 [포스팅]
  7. Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation, NAACL 2022 [포스팅]
  8. Partner Personas Generation for Dialogue Response Generation, NAACL 2022 [포스팅]
  9. Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting, NAACL 2022 [포스팅]
  10. 데이터세트

4.3.2 EMPATHETIC-DIALOGUES (Emotion-Chat)

  1. Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset, ACL 2019 [포스팅]
  2. EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systems, AAAI, FLAIRS 2020 [포스팅]
  3. CAiRE: An Empathetic Neural Chatbot, AAAI 2020 demo [포스팅]
  4. HappyBot: Generating Empathetic Dialogue Responses by Improving User Experience Look-ahead, ICASSP 2020 [포스팅] [링크]
  5. Towards Persona-Based Empathetic Conversational Models, EMNLP 2020 [포스팅]
  6. EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation, COLING 2020 [포스팅]
  7. Towards Empathetic Dialogue Generation over Multi-type Knowledge, Preprint 2020
  8. CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation, ACL 2020 [포스팅]
  9. EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning, LREC-COLING 2024 (Cogsci 2024 Review)

4.4 기타

  1. MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation, AAAI 2022 [포스팅]
  2. Fusing Task-oriented and Open-domain Dialogues in Conversational Agents, AAAI 2022 [포스팅]
  3. DialogLM: Pre-Trained Model for Long Dialogue Understanding and Summarization, AAAI 2022 [포스팅]
  4. DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations, ARR 2023.10 [포스팅]

5. Paraphrase Generation

5.1 Supervised

  1. A Deep Generative Framework for Paraphrase Generation, AAAI 2018 [포스팅]
  2. Learning Semantic Sentence Embeddings using Pair-wise Discriminator, COLING 2018 [포스팅]
  3. Paraphrasing with Large Language Models, WNGT 2019 [포스팅]
  4. Zero-Shot Paraphrase Generation with Multilingual Language Models, Preprint 2019 [포스팅]

5.2 Unsupervised

  1. Unsupervised paraphrase generation using pre-trained language models, Preprint 2020 [포스팅]
  2. Unsupervised Paraphrase Generation via Dynamic Blocking, Preprint 2020 [포스팅]
  3. Unsupervised Paraphrasing via Deep Reinforcement Learning, KDD 2020 [포스팅]
  4. CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling, AAAI 2019 [포스팅]
  5. Unsupervised Paraphrasing by Simulated Annealing, ACL 2020 [포스팅]
  6. Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning, AAAI 2022 [포스팅]
  7. Paraphrasing via Ranking Many Candidates, INLG 2022

6. Multimodal

  1. Generative Adversarial Text to Image Synthesis (2016, 05) [포스팅]
  2. Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation, CVPR 2021 [포스팅]
  3. Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems, ACL 2019 [포스팅]
  4. Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline, ECCV 2020 [포스팅]
  5. SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations, DSTC10-Track3 2021 [포스팅]
  6. Multimodal Interactions Using Pretrained Unimodal Models for SIMMC 2.0, DSTC10 at AAAI 2022
  7. PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling, ACL 2021 [포스팅]
  8. Multimodal Dialogue Response Generation, ACL 2022 [포스팅]
  9. Taming transformers for high-resolution image synthesis, CVPR 2021 [포스팅]
  10. Multi-modal Emotion and Cause Analysis in Modality-Switching Conversations: A New Task and the Benchmarks, ARR 2022.10 [포스팅]
  11. Multimodal Clustering for Multimodal Intent Discovery, ARR 2023.08 [포스팅]

7. MRC

  1. Bidirectional Attention Flow for Machine Comprehension. ICLR 2017. paper
  2. Attention-over-attention Neural Networks for Reading Comprehension. ACL 2017. [포스팅]
  3. R-NET: Machine Reading Comprehension with Self-matching Networks. Natural Language Computing Group, Microsoft Research Asia. paper
  4. Stochastic Answer Networks for Machine Reading Comprehension. ACL 2018. [포스팅]
  5. Ask the Right Questions: Active Question Reformulation with Reinforcement Learning. ICLR2018. paper
  6. Read + Verify: Machine Reading Comprehension with Unanswerable Questions. AAAI2019. [포스팅]
  7. SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering, Arxiv 2018.12, [포스팅]

8. NER

  1. Neural Architectures for Named Entity Recognition, NAACL 2016 [포스팅]
  2. Pooled Contextualized Embeddings for Named Entity Recognition, NAACL 2019 [포스팅]
  3. Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition, EMNLP 2019 [포스팅]
  4. Cloze-driven Pretraining of Self-attention Networks, EMNLP 2019 [포스팅]
  5. A Unified MRC Framework for Named Entity Recognition, ACL 2020 [포스팅]
  6. Named Entity Recognition with Context-Aware Dictionary Knowledge, CCL 2020 [포스팅]
  7. Zero-Resource Cross-Lingual Named Entity Recognition, AAAI 2020 [포스팅]
  8. LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention, EMNLP 2020 [포스팅]

9. Emotion Recognition

  1. Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances, ACCESS 2019 [포스팅]
  2. DialogueRNN: An Attentive RNN for Emotion Detection in Conversations, AAAI 2019 [포스팅]
  3. Modeling both context-and speaker-sensitive dependence for emotion detection in multi-speaker conversations, IJCAI 2019 [포스팅]
  4. Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations, EMNLP 2019 [포스팅]
  5. COSMIC: COmmonSense knowledge for eMotion Identification in Conversations, Findings of EMNLP 2020 [포스팅]
  6. Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network, AAAI 2020 [포스팅]
  7. Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations, EMNLP 2020 [포스팅]
  8. Graph Based Network with Contextualized Representations of Turns in Dialogue, EMNLP 2021 [포스팅]
  9. (ToDKaT) Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection, ACL 2021 [포스팅]
  10. (DAG-ERC) Directed Acyclic Graph Network for Conversational Emotion Recognition, ACL 2021 [포스팅]
  11. Hybrid Curriculum Learning for Emotion Recognition in Conversation, AAAI 2022 [포스팅]
  12. DialogueEIN: Emotional Interaction Network for Emotion Recognition in Conversations, ARR 2022.01 [포스팅]
  13. Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts, ARR 2022.01 [포스팅]
  14. CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation, NAACL 2022
  15. The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation, INTERSPEECH 2022
  16. M3GAT: A Multi-Modal Multi-Task Interactive GraphAttention Network for Conversational Sentiment Analysisand Emotion Recognition, TOIS 2022 Review [포스팅]
  17. Disentangled Variational Autoencoder for Dialogue Emotion Recognition, Transactions on Affective Computing (ARR 2022.12) [포스팅]
  18. Multi-domain Emotion Detection using Transfer Learning, ARR 2022.12 [포스팅]
  19. "We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations, ARR 2023.04 [포스팅]
  20. Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion, ARR 2023.10
  21. Multi-label Classification for Emotion Recognition in Conversation with Few-Shot Contrastive Learning, ARR 2023.10
  22. Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation, ARR Review 2312 [포스팅]
  23. Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation, ARR Review 2312
  24. PromptECL: Leveraging Prompt Engineering to Unlock Emotion Classiffcation Capabilities in LLMs, ARR Review 2505
  25. Spatiotemporal Emotion Reasoning: The Complete Picture of Emotion Recognition in Conversation via an Appraisal-Driven LLM-Encoder Framework, ARR Review 2505

9.1 Emotion Recognition Dataset

  1. IEMOCAP: Interactive emotional dyadic motion capture database, JLRE 2008
  2. The semaine database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent, IEEE Transactions on Affective Computing 2012
  3. (DailyDialog) DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset, AFNLP 2017
  4. (Emolines) Emotionlines: An emotion corpus of multi-party conversations, LREC 2018
  5. (EmoryNLP) Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks, In The AAAI Workshop on Affective Content Analysis, AFFCON'18, 2018.
  6. Understanding emotions in text using deep learning and big data, Computers in Human Behavior 2019
  7. MUStARD: Towards Multimodal Sarcasm Detection (An Obviously Perfect Paper), ACL 2019
  8. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations, ACL 2019 [포스팅]
  9. (EmoContext) SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text, SEMVAL 2019
  10. Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset, ACL 2020
  11. K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations, Arxiv 2020
  12. (ScenarioSA) ScenarioSA: A Dyadic Conversational Database for Interactive Sentiment Analysis, IEEE Access 2020
  13. EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators, LREC 2020 [포스팅]
  14. GoEmotions: A Dataset for Fine-Grained Emotion Classification, ACL 2020 [포스팅]

11. IR

11.1 Search Clarification

  • MIMICS: A Large-Scale Data Collection for Search Clarification, CIKM 2020 (Resource Track) [포스팅]
  • Learning Multiple Intent Representations for Search Queries, CIKM 2021 [포스팅]
  • Revisiting Open Domain Query Facet Extraction and Generation, ICTIR 2022 [포스팅]
  • Stochastic Optimization of Text Set Generation for Learning Multiple Query Intent Representations, CIKM 2022 [포스팅]
  • Query Sub-intent Mining by Incorporating Search Results with Query Logs for Information Retrieval, ICBDA 2023 [포스팅]
  • Improving Search Clarification with Structured Information Extracted from Search Results, KDD 2023 [포스팅]
  • Enhanced Facet Generation with LLM Editing, LREC-COLING 2024

11.2 Query Rewriting / Expansion

  • Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting, Findings of EMNLP 2023 [포스팅]
  • Query2doc: Query Expansion with Large Language Models, EMNLP 2023 [포스팅]
  • Asking Clarification Questions to Handle Ambiguity in Open-Domain QA, Findings of EMNLP 2023 [포스팅]
  • Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations, Findings of EMNLP 2023 [포스팅]
  • Query Expansion by Prompting Large Language Models, Preprint 2023 [포스팅]

11.3 Search Result Diversification

  • IntenT5: Search Result Diversification using Causal Language Models, Preprint 2021 [포스팅]
  • Search Result Diversification Using Query Aspects as Bottlenecks, CIKM 2023 [포스팅]
  • Knowledge Enhanced Search Result Diversification, SIGKDD 2022 [포스팅]
  • Exploiting query reformulations for web search result diversification. WWW 2010 [포스팅]

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