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 2. Sentence Mover's Similarity Automatic Evaluation for Multi-Sentence Texts. *ACL 2019*. [[PDF](https://pdfs.semanticscholar.org/7164/b4cb89b268dd4887fc029488393c4c249306.pdf)]
 
 ### NAACL2018
-Discourse-Aware Neural Rewards for Coherent Text Generation
+## Long Papers
+**Discourse-Aware Neural Rewards for Coherent Text Generation**
 *In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text.*
 
-Neural Text Generation in Stories Using Entity Representations as Context
+**Neural Text Generation in Stories Using Entity Representations as Context**
 *We introduce an approach to neural text generation that explicitly represents entities mentioned in the text.*
 
-A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
+**A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation**
 *We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model.*
 
-Natural Answer Generation with Heterogeneous Memory
+**Natural Answer Generation with Heterogeneous Memory**
 *In this work, we propose a novel attention mechanism to encourage the decoder to actively interact with the memory by taking its heterogeneity into account.*
 
-Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
+**Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation**
 *We present a neural model for question generation from knowledge graphs triples in a “Zero-shot” setup, that is generating questions for predicate, subject types or object types that were not seen at training time.*
 
-Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types
+**Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types**
 *We present a neural model for question generation from knowledge graphs triples in a “Zero-shot” setup, that is generating questions for predicate, subject types or object types that were not seen at training time.*
 
-What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis
+**What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis**
 *We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. We create a dataset that consists of movie scripts, attribute-value pairs for the movies’ aspects, as well as overviews, which we extract from an online database.*
 
-Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
+**Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions**
 *In this paper, we propose to study the problem of court view generation from the fact description in a criminal case.*
 
-Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
+**Adversarial Example Generation with Syntactically Controlled Paraphrase Networks**
 *We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples.*
 
-Dialog Generation Using Multi-Turn Reasoning Neural Networks
+**Dialog Generation Using Multi-Turn Reasoning Neural Networks**
 *In this paper, we propose a generalizable dialog generation approach that adapts multi-turn reasoning, one recent advancement in the field of document comprehension, to generate responses (“answers”) by taking current conversation session context as a “document” and current query as a “question”.*
 
-Neural Text Generation in Stories Using Entity Representations as Context
+**Neural Text Generation in Stories Using Entity Representations as Context**
 *We introduce an approach to neural text generation that explicitly represents entities mentioned in the text.*
 
-**short papers**
+## Short Papers
 
-Automatic Dialogue Generation with Expressed Emotions
+**Automatic Dialogue Generation with Expressed Emotions**
 *In this research, we address the problem of forcing the dialogue generation to express emotion.*
 
-Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
+**Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network**
 *We propose a guiding generation model that combines the extractive method and the abstractive method.*
 
-Natural Language Generation by Hierarchical Decoding with Linguistic Patterns
+**Natural Language Generation by Hierarchical Decoding with Linguistic Patterns**
 *This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size.*
 
-RankME: Reliable Human Ratings for Natural Language Generation
-**
+**RankME: Reliable Human Ratings for Natural Language Generation**
+*We present a novel rank-based magnitude estimation method (RankME), which combines the use of continuous scales and relative assessments.*
 
+**Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations**
+*Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation.*
+
+**Leveraging Context Information for Natural Question Generation**
+*We propose a model that matches the answer with the passage before generating the question.*
 
+**TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation**
+*In this paper, we present a novel approach TypeSQL which formats the problem as a slot filling task in a more reasonable way.*
 
+**Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata**
+*In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata.*
+
+**Natural Language to Structured Query Generation via Meta-Learning**
+*In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function.*