Isidora Tourni


2023

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Proceedings of the Seventh Widening NLP Workshop (WiNLP 2023)
Bonaventure F. P. Dossou | Isidora Tourni | Hatem Haddad | Shaily Bhatt | Fatemehsadat Mireshghallah | Sunipa Dev | Tanvi Anand | Weijia Xu | Atnafu Lambebo Tonja | Alfredo Gomez | Chanjun Park
Proceedings of the Seventh Widening NLP Workshop (WiNLP 2023)

2022

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Proceedings of the The Sixth Widening NLP Workshop (WiNLP)
Shaily Bhatt | Sunipa Dev | Bonaventure Dossou | Tirthankar Ghosal | Hatem Haddad | Haley M. Lepp | Fatemehsadat Mireshghallah | Surangika Ranathunga | Xanda Schofield | Isidora Tourni | Weijia Xu
Proceedings of the The Sixth Widening NLP Workshop (WiNLP)

2021

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Cultural and Geographical Influences on Image Translatability of Words across Languages
Nikzad Khani | Isidora Tourni | Mohammad Sadegh Rasooli | Chris Callison-Burch | Derry Tanti Wijaya
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across languages, translations can be identified via images associated with words in different languages that have a high degree of visual similarity. However, translating via images has been shown to improve upon text-only models only marginally. To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i.e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity. In addition, in line with previous works that show images help more in translating concrete words, we found that concrete words have improved image translatability compared to abstract ones.

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Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
Isidora Tourni | Lei Guo | Taufiq Husada Daryanto | Fabian Zhafransyah | Edward Edberg Halim | Mona Jalal | Boqi Chen | Sha Lai | Hengchang Hu | Margrit Betke | Prakash Ishwar | Derry Tanti Wijaya
Findings of the Association for Computational Linguistics: EMNLP 2021

News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.