Santiago Castro


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FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework
Santiago Castro | Ruoyao Wang | Pingxuan Huang | Ian Stewart | Oana Ignat | Nan Liu | Jonathan Stroud | Rada Mihalcea
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER – a novel dataset consisting of 28,000 videos and descriptions in support of this evaluation framework. The fill-in-the-blanks setting tests a model’s understanding of a video by requiring it to predict a masked noun phrase in the caption of the video, given the video and the surrounding text. The FIBER benchmark does not share the weaknesses of the current state-of-the-art language-informed video understanding tasks, namely: (1) video question answering using multiple-choice questions, where models perform relatively well because they exploit linguistic biases in the task formulation, thus making our framework challenging for the current state-of-the-art systems to solve; and (2) video captioning, which relies on an open-ended evaluation framework that is often inaccurate because system answers may be perceived as incorrect if they differ in form from the ground truth. The FIBER dataset and our code are available at


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WhyAct: Identifying Action Reasons in Lifestyle Vlogs
Oana Ignat | Santiago Castro | Hanwen Miao | Weiji Li | Rada Mihalcea
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We aim to automatically identify human action reasons in online videos. We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describing them. We introduce and make publicly available the WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons. We describe a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.


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LifeQA: A Real-life Dataset for Video Question Answering
Santiago Castro | Mahmoud Azab | Jonathan Stroud | Cristina Noujaim | Ruoyao Wang | Jia Deng | Rada Mihalcea
Proceedings of the Twelfth Language Resources and Evaluation Conference

We introduce LifeQA, a benchmark dataset for video question answering that focuses on day-to-day real-life situations. Current video question answering datasets consist of movies and TV shows. However, it is well-known that these visual domains are not representative of our day-to-day lives. Movies and TV shows, for example, benefit from professional camera movements, clean editing, crisp audio recordings, and scripted dialog between professional actors. While these domains provide a large amount of data for training models, their properties make them unsuitable for testing real-life question answering systems. Our dataset, by contrast, consists of video clips that represent only real-life scenarios. We collect 275 such video clips and over 2.3k multiple-choice questions. In this paper, we analyze the challenging but realistic aspects of LifeQA, and we apply several state-of-the-art video question answering models to provide benchmarks for future research. The full dataset is publicly available at

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HAHA 2019 Dataset: A Corpus for Humor Analysis in Spanish
Luis Chiruzzo | Santiago Castro | Aiala Rosá
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper presents the development of a corpus of 30,000 Spanish tweets that were crowd-annotated with humor value and funniness score. The corpus contains approximately 38.6% of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweets. The corpus has been used in an automatic humor recognition and analysis competition, obtaining encouraging results from the participants.


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Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
Santiago Castro | Devamanyu Hazarika | Verónica Pérez-Rosas | Roger Zimmermann | Rada Mihalcea | Soujanya Poria
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic classification of sarcasm. As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows. MUStARD consists of audiovisual utterances annotated with sarcasm labels. Each utterance is accompanied by its context of historical utterances in the dialogue, which provides additional information on the scenario where the utterance occurs. Our initial results show that the use of multimodal information can reduce the relative error rate of sarcasm detection by up to 12.9% in F-score when compared to the use of individual modalities. The full dataset is publicly available for use at


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A Crowd-Annotated Spanish Corpus for Humor Analysis
Santiago Castro | Luis Chiruzzo | Aiala Rosá | Diego Garat | Guillermo Moncecchi
Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

Computational Humor involves several tasks, such as humor recognition, humor generation, and humor scoring, for which it is useful to have human-curated data. In this work we present a corpus of 27,000 tweets written in Spanish and crowd-annotated by their humor value and funniness score, with about four annotations per tweet, tagged by 1,300 people over the Internet. It is equally divided between tweets coming from humorous and non-humorous accounts. The inter-annotator agreement Krippendorff’s alpha value is 0.5710. The dataset is available for general usage and can serve as a basis for humor detection and as a first step to tackle subjectivity.

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A High Coverage Method for Automatic False Friends Detection for Spanish and Portuguese
Santiago Castro | Jairo Bonanata | Aiala Rosá
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

False friends are words in two languages that look or sound similar, but have different meanings. They are a common source of confusion among language learners. Methods to detect them automatically do exist, however they make use of large aligned bilingual corpora, which are hard to find and expensive to build, or encounter problems dealing with infrequent words. In this work we propose a high coverage method that uses word vector representations to build a false friends classifier for any pair of languages, which we apply to the particular case of Spanish and Portuguese. The required resources are a large corpus for each language and a small bilingual lexicon for the pair.