huashen218/convxai-cia-dataset huggingface dataset:
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6{\%} error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6{\%} increase in average recall on the SemEval 2017 sentiment analysis dataset.
Entity-Centric Contextual Affective Analysis
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer attends on sentences that can semantically infer the claim on top of the first attention; and 3) An output layer predicts the verdict based on the embedded evidence. Experimental results on three public benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.
Predicting Human Activities from User-Generated Content
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.
You Write like You Eat: Stylistic Variation as a Predictor of Social Stratification
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Inspired by Labov{'}s seminal work on stylisticvariation as a function of social stratification,we develop and compare neural models thatpredict a person{'}s presumed socio-economicstatus, obtained through distant supervision,from their writing style on social media. Thefocus of our work is on identifying the mostimportant stylistic parameters to predict socio-economic group. In particular, we show theeffectiveness of morpho-syntactic features aspredictors of style, in contrast to lexical fea-tures, which are good predictors of topic
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents{'} social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.
Fine-Grained Spoiler Detection from Large-Scale Review Corpora
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: This paper presents computational approaches for automatically detecting critical plot twists in reviews of media products. First, we created a large-scale book review dataset that includes fine-grained spoiler annotations at the sentence-level, as well as book and (anonymized) user information. Second, we carefully analyzed this dataset, and found that: spoiler language tends to be book-specific; spoiler distributions vary greatly across books and review authors; and spoiler sentences tend to jointly appear in the latter part of reviews. Third, inspired by these findings, we developed an end-to-end neural network architecture to detect spoiler sentences in review corpora. Quantitative and qualitative results demonstrate that the proposed method substantially outperforms existing baselines.
Celebrity Profiling
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Celebrities are among the most prolific users of social media, promoting their personas and rallying followers. This activity is closely tied to genuine writing samples, which makes them worthy research subjects in many respects, not least profiling. With this paper we introduce the Webis Celebrity Corpus 2019. For its construction the Twitter feeds of 71,706 verified accounts have been carefully linked with their respective Wikidata items, crawling both. After cleansing, the resulting profiles contain an average of 29,968 words per profile and up to 239 pieces of personal information. A cross-evaluation that checked the correct association of Twitter account and Wikidata item revealed an error rate of only 0.6{\%}, rendering the profiles highly reliable. Our corpus comprises a wide cross-section of local and global celebrities, forming a unique combination of scale, profile comprehensiveness, and label reliability. We further establish the state of the art{'}s profiling performance by evaluating the winning approaches submitted to the PAN gender prediction tasks in a transfer learning experiment. They are only outperformed by our own deep learning approach, which we also use to exemplify celebrity occupation prediction for the first time.
Dataset Creation for Ranking Constructive News Comments
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task. However, most of them considered users{'} positive feedback, such as {``}Like{''}-button clicks, as a quality measure. In this paper, we address directly evaluating the quality of comments on the basis of {``}constructiveness,{''} separately from user feedback. To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores). Our experiments clarify that C-scores are not always related to users{'} positive feedback, and the performance of pairwise ranking models tends to be enhanced by the variation of comments rather than articles.
Enhancing Air Quality Prediction with Social Media and Natural Language Processing
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Accompanied by modern industrial developments, air pollution has already become a major concern for human health. Hence, air quality measures, such as the concentration of PM2.5, have attracted increasing attention. Even some studies apply historical measurements into air quality forecast, the changes of air quality conditions are still hard to monitor. In this paper, we propose to exploit social media and natural language processing techniques to enhance air quality prediction. Social media users are treated as social sensors with their findings and locations. After filtering noisy tweets using word selection and topic modeling, a deep learning model based on convolutional neural networks and over-tweet-pooling is proposed to enhance air quality prediction. We conduct experiments on 7-month real-world Twitter datasets in the five most heavily polluted states in the USA. The results show that our approach significantly improves air quality prediction over the baseline that does not use social media by 6.9{\%} to 17.7{\%} in macro-F1 scores.
Twitter Homophily: Network Based Prediction of User's Occupation
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user{'}s occupational class. We show that the content information of a user{'}s tweets, the profile descriptions of a user{'}s follower/following community, and the user{'}s social network provide useful information for classifying a user{'}s occupational group. In our study, we extend an existing data set for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this data set with just a small fraction of the training data.
Domain Adaptive Dialog Generation via Meta Learning
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Domain adaptation is an essential task in dialog system building because there are so many new dialog tasks created for different needs every day. Collecting and annotating training data for these new tasks is costly since it involves real user interactions. We propose a domain adaptive dialog generation method based on meta-learning (DAML). DAML is an end-to-end trainable dialog system model that learns from multiple rich-resource tasks and then adapts to new domains with minimal training samples. We train a dialog system model using multiple rich-resource single-domain dialog data by applying the model-agnostic meta-learning algorithm to dialog domain. The model is capable of learning a competitive dialog system on a new domain with only a few training examples in an efficient manner. The two-step gradient updates in DAML enable the model to learn general features across multiple tasks. We evaluate our method on a simulated dialog dataset and achieve state-of-the-art performance, which is generalizable to new tasks.
Strategies for Structuring Story Generation
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories.
Argument Generation with Retrieval, Planning, and Realization
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content.
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Recent neural language generation systems often \textit{hallucinate} contents (i.e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text. To mitigate this issue, we propose to integrate a language understanding module for data refinement with self-training iterations to effectively induce strong equivalence between the input data and the paired text. Experiments on the E2E challenge dataset show that our proposed framework can reduce more than 50{\%} relative unaligned noise from the original data-text pairs. A vanilla sequence-to-sequence neural NLG model trained on the refined data has improved on content correctness compared with the current state-of-the-art ensemble generator.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple modal contents. For instance, graphic news contains plenty of images in addition to text. Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information. To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents. We construct a large-scale dataset for this task and explore several representative methods. Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information. Evaluation results show that our proposed model can achieve better performance than competitive baselines.
A Working Memory Model for Task-oriented Dialog Response Generation
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the {``}activated{''} contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: We propose a new CogQA framework for multi-hop reading comprehension question answering in web-scale documents. Founded on the dual process theory in cognitive science, the framework gradually builds a \textit{cognitive graph} in an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides explainable reasoning paths. Specifically, our implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning joint $F_1$ score of 34.9 on the leaderboard, compared to 23.1 of the best competitor.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co-attention and self-attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based single model delivers competitive result, and the ensemble model achieves the state-of-the-art performance.
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context. To achieve this, we propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr). First, the Document Explorer iteratively selects relevant documents and represents divergent reasoning chains in a tree structure so as to allow assimilating information from all chains. The Answer Proposer then proposes an answer from every root-to-leaf path in the reasoning tree. Finally, the Evidence Assembler extracts a key sentence containing the proposed answer from every path and combines them to predict the final answer. Intuitively, EPAr approximates the coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents. We jointly optimize our 3 modules by minimizing the sum of losses from each stage conditioned on the previous stage{'}s output. On two multi-hop reading comprehension datasets WikiHop and MedHop, our EPAr model achieves significant improvements over the baseline and competitive results compared to the state-of-the-art model. We also present multiple reasoning-chain-recovery tests and ablation studies to demonstrate our system{'}s ability to perform interpretable and accurate reasoning.