Papers with timing
Building and curating conversational corpora for diversity-aware language science and technology (2022.lrec-1)
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| Challenge: | Language resources that capture language use in its natural habitat of social interaction are rare despite the obvious merits of studying the very environment where we all learn and use it everyday. |
| Approach: | They propose to build an analysis pipeline and best practice guidelines for building and curating corpora of everyday conversation in diverse languages. |
| Outcome: | The proposed pipeline can be used to collect and curate conversational corpora in 67 languages and varieties from 28 phyla. |
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
| Approach: | They propose a disjunctive model that computes the contribution of content and context separately. |
| Outcome: | The proposed model outperforms baseline models which use popularity as informativeness measure. |
Losses that Cook: Topological Optimal Transport for Structured Recipe Generation (2026.findings-acl)
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| Challenge: | Existing work on cooking recipes relies on cross-entropy, but it does not address holistic composition of ingredient sets and numerical aspects of recipes. |
| Approach: | They propose a topological loss that represents ingredient lists as point clouds in embedding space . they show that the Dice loss excels in time/temperature precision . |
| Outcome: | The proposed model improves ingredient- and action-level metrics while preserving time/temperature precision. |
A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior (2025.acl-long)
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| Challenge: | Standard models that focus on fixation durations ignore spatial dynamics of reading . authors propose a model that captures how long fixations last, where they land and when . |
| Approach: | They propose a generative model that captures how long fixations last and where they land and when they occur. |
| Outcome: | The proposed model exhibits higher likelihood on held-out reading data than baselines. |