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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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CSCML 2022, ICMC 20212022Traditionally, the most data-heavy part of a (D)TLS handshake has been authentication which includes a handshake signature and digital certificates. Although most common (D)TLS usecases are not significantly affected, some constrained ones such as low bandwidth environments or delay sensitive applications can see drastic performance degradation due to big certificates or certificate chains. That has led
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NAACL 20222022Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation
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ICLR 2022 Workshop on DL4C2022Traditional bug detection mechanisms have focused on a limited set of important issues and have specialized detectors for each of them. As the code corpora continue to grow in size and complexity, newer opportunities for a developer to make mistakes emerge, leading to long tail of local bugs. Hence, we must investigate generalizable approaches that can detect such bugs. In this paper, we formulate and use
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NAACL 20222022Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. Recent studies show that these tasks benefit from modeling dependencies across multiple candidate sentences jointly. In this paper, we first show that popular pre-trained transformers perform poorly when used for fine-tuning on
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ACL 2022 Workshop on Insights from Negative Results in NLP2022Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes. We experiment with augmenting a transformer model for this task with modules that effectively utilize a wider field of view and
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Gretchen Ertl
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