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1. Improving the Identification of the Discourse Function of News Article Paragraphs

   https://doi.org/10.18653/v1/2020.nuse-1.3  

   Banisakher, Deya; Yarlott, W. Victor; Aldawsari, Mohammed; Rishe, Naphtali; Finlayson, Mark (January 2020, 1st Joint Workshop on Narrative Understanding, Storylines, and Events (NUSE 2020))

   null (Ed.)

   Identifying the discourse structure of documents is an important task in understanding written text. Building on prior work, we demonstrate an improved approach to automatically identifying the discourse function of paragraphs in news articles. We start with the hierarchical theory of news discourse developed by van Dijk (1988) which proposes how paragraphs function within news articles. This discourse information is a level intermediate between phrase- or sentence-sized discourse segments and document genre, characterizing how individual paragraphs convey information about the events in the storyline of the article. Specifically, the theory categorizes the relationships between narrated events and (1) the overall storyline (such as Main Events, Background, or Consequences) as well as (2) commentary (such as Verbal Reactions and Evaluations). We trained and tested a linear chain conditional random field (CRF) with new features to model van Dijk’s labels and compared it against several machine learning models presented in previous work. Our model significantly outperformed all baselines and prior approaches, achieving an average of 0.71 F1 score which represents a 31.5% improvement over the previously best-performing support vector machine model. 

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2. Lifting Weak Supervision To Structured Prediction

   Vishwakarma, H; Sala, F (January 2022, Advances in neural information processing systems)

   Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from various sources. WS is theoretically well-understood for binary classification, where simple approaches enable consistent estimation of pseudolabel noise rates. Using this result, it has been shown that downstream models trained on the pseudolabels have generalization guarantees nearly identical to those trained on clean labels. While this is exciting, users often wish to use WS for\emph {structured prediction}, where the output space consists of more than a binary or multi-class label set: eg rankings, graphs, manifolds, and more. Do the favorable theoretical properties of WS for binary classification lift to this setting? We answer this question in the affirmative for a wide range of scenarios. For labels taking values in a finite metric space, we introduce techniques new to weak supervision based on pseudo-Euclidean embeddings and tensor decompositions, providing a nearly-consistent noise rate estimator. For labels in constant-curvature Riemannian manifolds, we introduce new invariants that also yield consistent noise rate estimation. In both cases, when using the resulting pseudolabels in concert with a flexible downstream model, we obtain generalization guarantees nearly identical to those for models trained on clean data. Several of our results, which can be viewed as robustness guarantees in structured prediction with noisy labels, may be of independent interest. 

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3. Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation

   Zhang, Yigeng; Yang, Fan; Zhang, Yifan; Mukherjee, Arjun (October 2020, . International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation)

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   Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be mistaken as factual news, due to its deceptive character. We found that in satirical news, the lexical and pragmatical attributes of the context are the key factors in amusing the readers. In this work, we propose a method that differentiates the satirical news and true news. It takes advantage of satirical writing evidence by leveraging the difference between the prediction loss of two language models, one trained on true news and the other on satirical news, when given a new news article. We compute several statistical metrics of language model prediction loss as features, which are then used to conduct downstream classification. The proposed method is computationally effective because the language models capture the language usage differences between satirical news documents and traditional news documents, and are sensitive when applied to documents outside their domains. 

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4. Prosody Prediction from Syntactic, Lexical, and Word Embedding Features

   https://doi.org/10.21437/SSW.2019-48  

   Sloan, Rose; Akhtar, Syed Sarfaraz; Li, Bryan; Shrivastava, Ritvik; Gravano, Agustin; Hirschberg, Julia (September 2019, 10th ISCA Speech Synthesis Workshop)

   Accurate prosody prediction from text leads to more natural-sounding TTS. In this work, we employ a new set of fea- tures to predict ToBI pitch accent and phrase boundaries from text. We investigate a wide variety of text-based features, in- cluding many new syntactic features, several types of word em- beddings, co-reference features, LIWC features, and specificity information. We focus our work on the Boston Radio News Corpus, a ToBI-labeled corpus of relatively clean news broad- casts, but also test our classifiers on Audix, a smaller corpus of read news, and on the Columbia Games Corpus, a corpus of conversational speech, in order to test the applicability of our model in cross-corpus settings. Our results show strong per- formance on both tasks, as well as some promising results for cross-corpus applications of our models. 

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5. Explaining Text Matching on Neural Natural Language Inference

   https://doi.org/10.1145/3418052  

   Kim, Youngwoo; Jang, Myungha; Allan, James (October 2020, ACM Transactions on Information Systems)

   null (Ed.)

   Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose lack of interpretability prohibits both straightforward integration and diagnosis for further improvement. We target the task of generating token-level explanations for NLI from a neural model. Many existing approaches for token-level explanation are either computationally costly or require additional annotations for training. In this article, we first introduce a novel method for training an explanation generator that does not require additional human labels. Instead, the explanation generator is trained with the objective of predicting how the model’s classification output will change when parts of the inputs are modified. Second, we propose to build an explanation generator in a multi-task learning setting along with the original NLI task so the explanation generator can utilize the model’s internal behavior. The experiment results suggest that the proposed explanation generator outperforms numerous strong baselines. In addition, our method does not require excessive additional computation at prediction time, which renders it an order of magnitude faster than the best-performing baseline. 

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