Fall 2022 Schedule

Location: 279, Fleming Building.

Zoom link: https://cuboulder.zoom.us/j/97014876908

Date	Title
9.1.21	Planning, introductions, welcome! CompSem meetings will be hybrid this semester - in person at Fleming 279, and online here: https://cuboulder.zoom.us/j/97014876908
9.8.21	10am (NOTE: special start time) Yoshinari Fujinuma thesis defense Analysis and Applications of Cross-Lingual Models in Natural Language Processing Human languages vary in terms of both typologically and data availability. A typical machine learning-based approach for natural language processing (NLP) requires training data from the language of interest. However, because machine learning-based approaches heavily rely on the amount of data available in each language, the quality of trained model languages without a large amount of data is poor. One way to overcome the lack of data in each language is to conduct cross-lingual transfer learning from resource-rich languages to resource-scarce languages. Cross-lingual word embeddings and multilingual contextualized embeddings are commonly used to conduct cross-lingual transfer learning. However, the lack of resources still makes it challenging to either evaluate or improve such models. This dissertation first proposes a graph-based method to overcome the lack of evaluation data in low-resource languages by focusing on the structure of cross-lingual word embeddings, further discussing approaches to improve cross-lingual transfer learning by using retrofitting methods and by focusing on a specific task. Finally, it provides an analysis of the effect of adding different languages when pretraining multilingual models.
9.15.21	ACL best paper recaps
9.22.21	Introduction to AI Institute (short talks)
9.29.21	*** CANCELLED ***
10.6.21	Invited talk: Artemis Panagopoulou, University of Pennsylvania Metaphor and Entailment: Looking at metaphors through the lens of textual entailment Metaphors are very intriguing elements of human language that are surprisingly prevalent in our everyday communications. Humans are pretty good at understanding metaphors, even if it is the first time they encounter them. Empirical studies indicate that 20% of our daily language use is metaphorical. Naturally, the ubiquity of metaphors draw the attention of psychologists who showed that the human brain processes conventional metaphors in the same speed as literal language. Nevertheless, the computational linguistics literature consistently treats metaphors as a separate domain to literal language. Earlier work has shown that traditional pipelines do not perform well on metaphoric datasets. Synchronously, the literature on computational understanding of metaphors has largely focused on developing concrete metaphor detection systems, coupled with interpretation systems targeted solely on metaphors. This tendency has presented across various aspects of the field, such as the purposeful exclusion of figurative language from large scale datasets. This study investigates the potential of constructing systems that can jointly handle metaphoric and literal sentences by leveraging the newfound capabilities of deep learning systems. We narrow the scope of the report, following earlier work, to evaluate deep learning systems fine-tuned on the task of textual entailment (TE). We argue that TE is a task naturally suited to the interpretation of metaphoric language. We show that TE systems can improve significantly in metaphoric performance by being fine-tuned on a small dataset with metaphoric premises. Even though the improvement in performance on metaphors is typically accompanied by a drop in performance on the original dataset we note that auto-regressive models seem to show a smaller drop in performance on literal examples compared to other types of models.
10.13.21	Invited guest: Arya McCarthy
10.20.21
10.27.21	Invited talk: Lisa Miracchi
11.3.21	EMNLP practice talks
11.10.21	EMNLP - no meeting
11.17.21	Elizabeth Spaulding prelim
11.24.21	Fall break - no meeting
12.1.21	Invited talk: Abe Handler
12.8.21	Abhidip Bhattacharyya proposal defense

Past Schedules

• Spring 2021 Schedule
• Fall 2020 Schedule
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• Fall 2019 Schedule
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