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Building a fine-grained subjectivity lexicon from a web corpus
AbstractIn this paper we propose a method to build fine-grained subjectivity lexicons including nouns, verbs and adjectives. The method, which is applied for Dutch, is based on the comparison of …...
Named Entity Recognition using Cross-lingual Resources: Arabic as an Example
Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can general- ize to previously unseen named entities. One such language is Arabic, which: …...
Mixed domain vs. multi-domain statistical machine translation
Domain adaptation boosts translation quality on in-domain data, but translation quality for domain adapted systems on out-of-domain data tends to suffer. Users of web-based translation services expect high quality translation …...
Distributionally Robust Language Modeling
AbstractLanguage models are generally trained on data spanning a wide range of topics (e.g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e.g., …...
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
AbstractNeural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on robustness …...
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
AbstractLanguage models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it …...
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
AbstractRetrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized …...