论文列表——NAACL 2019

NAACL 2019大会的论文列表已公布,本文将分享一些值得关注的研究,为后续深入阅读提供参考。

最近NAACL-2019接收列表出了,列出感兴趣的paper,供之后阅读:

名称类型关键字阅读价值笔记
Deep Adversarial Learning for NLPtutorial
Transfer Learning in Natural Language Processingtutorial
Deep Learning for Natural Language Inferencetutorial
A Large-Scale Comparison of Historical Text Normalization Systems比较总结
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC数据集
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks摘要
Abusive Language Detection with Graph Convolutional NetworksGNN
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction生成、强化学习
Adaptive Convolution for Text Classification分类
Adversarial Training for Weakly Supervised Event Detection清华、对抗
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models预训练
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction生成
Attention is not Explanationattention
Detecting Cybersecurity Events from Noisy Short Text实际问题
Detection of Abusive Language: the Problem of Biased Datasets数据集
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusiondiscourse
Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classificationdiscourse marker
A corpus of text-image discourse relationsdiscourse
Learning Hierarchical Discourse-level Structure for Fake News Detectiondiscourse、fake news
Mining Discourse Markers for Unsupervised Sentence Representation Learningdiscourse marker
Document-Level Event Factuality Identification via Adversarial Neural Network事件真实性、ANN
Does My Rebuttal Matter? Insights from a Major NLP ConferenceREBUTTAL、MAJOR?
Early Rumour Detectionrumor detection
Evaluating Style Transfer for Textstyle transfer、evaluation
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translationback-translation
Fake News Detection using Deep Markov Random Fieldsfake news detection
Generalizing Unmasking for Short Textsunmasking?
Harry Potter and the Action Prediction Challenge from Natural LanguageHarry Potter?
How Large A Vocabulary Does Text Classification Need? A Variational Approach on Vocabulary Selection分类
Incorporating Emoji Descriptions Improves Tweet Classification分类、tweet
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification分类、zero-shot
Keyphrase Generation: A Text Summarization Struggle生成
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation生成
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution NetworksKG、GNN
Modeling Recurrence for TransformerTransformer
Neural Text Generation from Rich Semantic Representations生成
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningRL、Graph
Positional Encoding to Control Output Sequence Length
Pragmatically Informative Text Generation生成
Pre-trained language model representations for language generation预训练语言模型、生成
Predicting the Type and Target of Offensive Posts in Social Mediaoffensive
Ranking-Based AutoEncoder for Extreme Multi-label ClassificationAE、multi-label
Recursive Routing Networks: Learning to Compose Modules for Language Understandingrecursive、understanding
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpusstyle transfer、RL
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN关系分类
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlationsstyle
Separating Planning from Realization in Neural Data to Text Generation生成
Star-TransformerTransformer
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation数据增强
Targeted Aspect-Based Sentiment Analysis as a Sentence Pair Classification Task: Constructing the Other Sentencesentence pair、生成
Text Generation from Knowledge Graphs生成、KG
Topic-Guided Variational Auto-Encoder for Text Generation生成、VAE
Towards Content Transfer through Grounded Text Generation生成、transfer
Training data augmentation for context-sensitive neural lemmatizer using inflection tables and raw text数据增强
Tweet Stance Detection Using an Attention based Neural Ensemble Modelstance detection
Unifying Human and Statistical Evaluation for Natural Language Generation生成、evaluation
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-EncodersAE、recursive

TO BE CONTINUED…

基于Transformer的对话系统是自然语言处理领域的重要研究方向,广泛应用于智能客服、虚拟助手、聊天机器人等场景。Transformer模型通过自注意力机制(Self-Attention)能够有效捕捉长距离依赖关系,提升对话上下文建模能力,因此成为构建对话系统的核心架构之一。 在对话系统中,Transformer被用于任务型对话系统(Task-Oriented Dialogue Systems)和开放域对话系统(Open-Domain Dialogue Systems)[^1]。任务型对话系统旨在完成特定任务,如订票、查询天气等,通常包括自然语言理解(NLU)、对话状态追踪(DST)、对话策略学习(DP)和自然语言生成(NLG)等模块。开放域对话系统则更关注生成自然、连贯的对话内容,常见于社交机器人和聊天应用。 近年来,基于Transformer的对话系统研究主要集中在以下几个方向: ### 模型结构优化 研究人员提出了多种改进的Transformer结构以增强对话建模能力。例如,**DialoGPT** 是基于GPT-2的对话生成模型,通过在大规模对话数据上进行预训练,实现了流畅的多轮对话生成 [^1]。**UniLM**(Unified Language Model)则通过统一的预训练目标,支持文本生成与理解任务,适用于对话系统中的多任务建模 [^1]。 ### 上下文建模与记忆机制 为了更好地捕捉对话历史信息,一些研究引入了记忆网络(Memory Networks)或层级Transformer结构。例如,**HRED**(Hierarchical Recurrent Encoder-Decoder)采用层级结构建模对话历史,而**Transformer Memory**则通过外部记忆模块增强上下文建模能力 [^1]。 ### 对话策略学习与强化学习 在任务型对话系统中,强化学习(Reinforcement Learning, RL)常用于优化对话策略。例如,**Deep Q-Networks**(DQN)与**Policy Gradient Methods**被用于学习最优动作选择,提升任务完成率 [^1]。结合Transformer与强化学习的方法,如**Transformer-based RL Policy Networks**,也取得了良好的效果。 ### 开放域对话生成 在开放域对话系统中,Transformer被广泛用于生成多样且连贯的回应。**Meena** 和 **BlenderBot** 是 Facebook 与 Google 推出的基于Transformer的对话模型,分别通过大规模训练数据与混合专家模型(Mixture of Experts)提升对话质量 [^1]。 ### 相关论文推荐 以下是一些具有代表性的基于Transformer的对话系统研究论文: - **"DialoGPT: Large-Scale Generative Pre-training for Conversation"** (2020) —— Microsoft 提出基于GPT-2的对话生成模型,在多轮对话中表现出色 。 - **"A Persona-Based Neural Conversation Model"** (2018) —— ACL 引入个性化信息建模机制,提升对话系统的人格一致性 。 - **"Generating Responses with a Focus Mechanism"** (2019) —— NAACL 提出焦点机制,增强对话回应的相关性与多样性 。 - **"Heterogeneous Graph Networks for Dialogue Understanding"** (2021) —— EMNLP 结合图神经网络与Transformer,建模对话中的复杂语义关系 [^1]。 - **"BlenderBot 2.0: Dialogue Research Meets Real-World Applications"** (2021) —— Meta 支持长期记忆与知识检索的对话系统 [^1]。 ### 示例代码 以下是一个基于 Hugging Face Transformers 的简单对话生成示例: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # 加载预训练 DialoGPT 模型和分词器 model_name = "microsoft/DialoGPT-medium" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # 用户输入 user_input = "你好,你今天过得怎么样?" input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt') # 生成回应 response_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True) print("Bot:", response) ```
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