ACL2018优质论文合集

[{"title":"（ 37 个子文件 19.52MB ） ACL2018优质论文合集","children":[{"title":"ACL2018优质论文合集","children":[{"title":"百度","children":[{"title":"Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task.pdf <span style='color:#111;'> 139.61KB </span>","children":null,"spread":false},{"title":"DuReader a Chinese Machine Reading Comprehension Dataset from Real-world Applications.pdf <span style='color:#111;'> 481.35KB </span>","children":null,"spread":false},{"title":"Joint Training of Candidate Extraction and Answer Selection in Reading Comprehension.pdf <span style='color:#111;'> 384.39KB </span>","children":null,"spread":false},{"title":"Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification.pdf <span style='color:#111;'> 399.03KB </span>","children":null,"spread":false}],"spread":true},{"title":"理解最先进的模型","children":[{"title":"Breaking NLI Systems with Sentences that Require Simple Lexical Inferences.pdf <span style='color:#111;'> 112.43KB </span>","children":null,"spread":false},{"title":"Did the Model Understand the Question.pdf <span style='color:#111;'> 565.23KB </span>","children":null,"spread":false}],"spread":true},{"title":"最佳论文","children":[{"title":"Learning to Ask Good Questions Ranking Clarification Questions using Neural Expected Value of Perfect Information.pdf <span style='color:#111;'> 726.39KB </span>","children":null,"spread":false},{"title":"Let’s do it “again” A First Computational Approach to Detecting Adverbial Presupposition Triggers.pdf <span style='color:#111;'> 248.64KB </span>","children":null,"spread":false},{"title":"Finding syntax in human encephalography with beam search.pdf <span style='color:#111;'> 468.19KB </span>","children":null,"spread":false},{"title":"Know What You Don't Know Unanswerable Questions for SQuAD.pdf <span style='color:#111;'> 894.63KB </span>","children":null,"spread":false}],"spread":true},{"title":"用多种语言和资源较少的语言评价","children":[{"title":"A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.pdf <span style='color:#111;'> 328.17KB </span>","children":null,"spread":false},{"title":"Triangular Architecture for Rare Language Translation.pdf <span style='color:#111;'> 514.64KB </span>","children":null,"spread":false},{"title":"On the Limitations of Unsupervised Bilingual Dictionary Induction.pdf <span style='color:#111;'> 604.37KB </span>","children":null,"spread":false}],"spread":true},{"title":"强基线","children":[{"title":"Unsupervised Random Walk Sentence Embeddings A Strong but Simple Baseline.pdf <span style='color:#111;'> 319.82KB </span>","children":null,"spread":false},{"title":"Strong Baselines for Neural Semi-supervised Learning under Domain Shift.pdf <span style='color:#111;'> 491.24KB </span>","children":null,"spread":false},{"title":"Baseline Needs More Love On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms.pdf <span style='color:#111;'> 886.62KB </span>","children":null,"spread":false}],"spread":true},{"title":"更难的数据集","children":[{"title":"The NarrativeQA Reading Comprehension Challenge.pdf <span style='color:#111;'> 219.96KB </span>","children":null,"spread":false},{"title":"Constructing Datasets for Multi-hop Reading Comprehension Across Documents.pdf <span style='color:#111;'> 812.71KB </span>","children":null,"spread":false}],"spread":true},{"title":"改进评估方法","children":[{"title":"The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing.pdf <span style='color:#111;'> 157.98KB </span>","children":null,"spread":false},{"title":"Improving Text-to-SQL Evaluation Methodology.pdf <span style='color:#111;'> 318.07KB </span>","children":null,"spread":false},{"title":"The price of debiasing automatic metrics in natural language evaluation.pdf <span style='color:#111;'> 1.26MB </span>","children":null,"spread":false}],"spread":true},{"title":"探索模型","children":[{"title":"Numeracy for Language Models Evaluating and Improving their Ability to Predict Numbers.pdf <span style='color:#111;'> 2.41MB </span>","children":null,"spread":false},{"title":"Deep-speare A Joint Neural Model of Poetic Language, Meter and Rhyme.pdf <span style='color:#111;'> 548.97KB </span>","children":null,"spread":false},{"title":"LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better.pdf <span style='color:#111;'> 979.15KB </span>","children":null,"spread":false},{"title":"Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers.pdf <span style='color:#111;'> 374.82KB </span>","children":null,"spread":false},{"title":"What you can cram into a single vector Probing sentence embeddings for linguistic properties.pdf <span style='color:#111;'> 439.70KB </span>","children":null,"spread":false},{"title":"Deep RNNs Encode Soft Hierarchical Syntax.pdf <span style='color:#111;'> 932.71KB </span>","children":null,"spread":false},{"title":"LSTMs Exploit Linguistic Attributes of Data.pdf <span style='color:#111;'> 237.77KB </span>","children":null,"spread":false},{"title":"Exploring Semantic Properties of Sentence Embeddings.pdf <span style='color:#111;'> 222.31KB </span>","children":null,"spread":false}],"spread":true},{"title":"对抗性实例","children":[{"title":"HotFlip White-Box Adversarial Examples for Text Classification.pdf <span style='color:#111;'> 112.46KB </span>","children":null,"spread":false},{"title":"Towards Robust Neural Machine Translation.pdf <span style='color:#111;'> 383.04KB </span>","children":null,"spread":false},{"title":"Adversarial Contrastive Estimation.pdf <span style='color:#111;'> 641.32KB </span>","children":null,"spread":false}],"spread":true},{"title":"腾讯","children":[{"title":"Towards Robust Neural Machine Translation.pdf <span style='color:#111;'> 383.04KB </span>","children":null,"spread":false},{"title":"Transformation Networks for Target-Oriented Sentiment Classification.pdf <span style='color:#111;'> 445.94KB </span>","children":null,"spread":false},{"title":"hyperdoc2vec Distributed Representations of Hypertext Documents.pdf <span style='color:#111;'> 1.71MB </span>","children":null,"spread":false},{"title":"Learning Domain-Sensitive and Sentiment-Aware Word Embeddings.pdf <span style='color:#111;'> 182.40KB </span>","children":null,"spread":false},{"title":"Automatic Article Commenting the Task and Dataset.pdf <span style='color:#111;'> 2.63MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]