Competition
NL4Opt: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison · Timothy Yu · Giuseppe Carenini · Bissan Ghaddar · Raymond Li · Shiqi He · Haley Li · Amin Banitalebi-Dehkordi · Zirui Zhou · Yong Zhang
Virtual
We propose a competition for extracting the meaning and formulation of an optimization problem based on its text description. For this competition, we have created the first dataset of linear programming (LP) word problems. A deep understanding of the problem description is an important first step towards generating the problem formulation. Therefore, we present two challenging sub-tasks for the participants. For the first sub-task, the goal is to recognize and label the semantic entities that correspond to the components of the optimization problem. For the second sub-task, the goal is to generate a meaning representation (i.e. a logical form) of the problem from its description and its problem entities. This intermediate representation of an LP problem will be converted to a canonical form for evaluation. The proposed task will be attractive because of its compelling application, the low-barrier to entry of the first sub-task, and the new set of challenges the second sub-task brings to semantic analysis and evaluation. The goal of this competition is to increase the access and usability of optimization solvers, allowing non-experts to solve important problems from various industries. In addition, this new task will promote the development of novel machine learning applications and datasets for operations research.
Schedule
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Wed 5:00 p.m. - 5:15 p.m.
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Introduction
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Overview
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SlidesLive Video This presentation summarizes the competition. It presents (1) the background and motivation, (2) tasks of the competition, (3) dataset and baselines, and (4) competition logistics. |
Rindranirina Ramamonjison · Timothy Yu · Giuseppe Carenini · Bissan Ghaddar · Amin Banitalebi-Dehkordi · Zirui Zhou · Yong Zhang 🔗 |
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Wed 5:15 p.m. - 5:30 p.m.
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Announcing Winners
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Announcement
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Announce the five winners of each task. |
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Wed 5:30 p.m. - 5:45 p.m.
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1st place NER (Infrrd AI Lab)
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Presentation
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Participants: JiangLong He, Mamatha N., Shiv Vignesh, Deepak Kumar, Akshay Uppal Infrrd AI Lab is the first-place winner of the NER task. Their team will be presenting a 10-minute presentation followed by 5 minutes reserved for Q&A. |
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Wed 5:45 p.m. - 6:00 p.m.
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2nd place NER (mcmc)
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Presentation
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Participants: Kangxu Wang, Ze Chen, Jiewen Zheng Team mcmc is the second-place winner of the NER task. Their team will be presenting a 10-minute presentation followed by 5 minutes reserved for Q&A. |
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Wed 6:00 p.m. - 6:15 p.m.
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Invited NER (VTCC-NLP)
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Presentation
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Participant: Xuan-Dung Doan VTCC-NLP is the 5th place winner of the NER task. They will be presenting a 10-minute presentation followed by 5 minutes reserved for Q&A. |
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Wed 6:15 p.m. - 6:30 p.m.
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1st place generation (UIUC-NLP)
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Presentation
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Participants: Neeraj Gangwar, Nickvash Kani Team UIUC-NLP is the first-place winner of the Generation task. Their team will be presenting a 10-minute presentation followed by 5 minutes reserved for Q&A. |
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Wed 6:30 p.m. - 6:45 p.m.
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2nd place generation (Sjang)
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Presentation
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Participant: Sanghwan Jang Sjang is the second-place winner of the NER task. The team will be presenting a 10-minute presentation followed by 5 minutes reserved for Q&A. |
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Wed 6:45 p.m. - 7:00 p.m.
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3rd place generation (Long)
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Presentation
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Participants: Jiayu Liu, Longhu Qin, Yuting Ning, Tong Xiao, Shangzi Xue, Zhenya Huang, Qi Liu, Enhong Chen, Jinze Wu Team Long is the third-place winner of the NER task. Their team will be presenting a 10-minute presentation followed by 5 minutes reserved for Q&A. |
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Wed 7:00 p.m. - 8:00 p.m.
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Poster session
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Posters
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Poster session with all winners and invited teams from both sub-tasks. |
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