Search Results for author:
Found 22 papers, 8 papers with code
Subspace Regularizers for Few-Shot Class Incremental Learning
The key to this approach is a new family of subspace regularization schemes that encourage weight vectors for new classes to lie close to the subspace spanned by the weights of existing classes.
"Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks
Our captioning results on Arabic are slightly better than that of its supervised model.
Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
Obtaining high-quality parallel corpora is of paramount importance for training NMT systems.
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data
We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research.
Cultural and Geographical Influences on Image Translatability of Words across Languages
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.
Cultural Vocal Bursts Intensity Prediction
Multilingual NLP
+3
Learning Translations via Images with a Massively Multilingual Image Dataset
To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents.
Learning Translations via Matrix Completion
Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora.
Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events
Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public.
Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding U.S. Gun Violence
We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U. S. between 2016 and 2018.
Multi-Label and Multilingual News Framing Analysis
News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation.
Sentiment-based Candidate Selection for NMT
The explosion of user-generated content (UGC)--e. g. social media posts, comments, and reviews--has motivated the development of NLP applications tailored to these types of informal texts.
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
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.
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks
Our captioning results on Arabic are slightly better than that of its supervised model.
Resolving Pronouns in Twitter Streams: Context can Help!
Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event.
An Unsupervised Approach to Discover Media Frames
We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.
BU-NEmo: an Affective Dataset of Gun Violence News
As such, we introduce a U. S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings.
Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show
While popular Television (TV) shows are airing, some users interested in these shows publish social media posts about the show.
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph.
