Anna Shvets


2022

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GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann | Abhik Bhattacharjee | Abinaya Mahendiran | Alex Wang | Alexandros Papangelis | Aman Madaan | Angelina Mcmillan-major | Anna Shvets | Ashish Upadhyay | Bernd Bohnet | Bingsheng Yao | Bryan Wilie | Chandra Bhagavatula | Chaobin You | Craig Thomson | Cristina Garbacea | Dakuo Wang | Daniel Deutsch | Deyi Xiong | Di Jin | Dimitra Gkatzia | Dragomir Radev | Elizabeth Clark | Esin Durmus | Faisal Ladhak | Filip Ginter | Genta Indra Winata | Hendrik Strobelt | Hiroaki Hayashi | Jekaterina Novikova | Jenna Kanerva | Jenny Chim | Jiawei Zhou | Jordan Clive | Joshua Maynez | João Sedoc | Juraj Juraska | Kaustubh Dhole | Khyathi Raghavi Chandu | Laura Perez Beltrachini | Leonardo F . R. Ribeiro | Lewis Tunstall | Li Zhang | Mahim Pushkarna | Mathias Creutz | Michael White | Mihir Sanjay Kale | Moussa Kamal Eddine | Nico Daheim | Nishant Subramani | Ondrej Dusek | Paul Pu Liang | Pawan Sasanka Ammanamanchi | Qi Zhu | Ratish Puduppully | Reno Kriz | Rifat Shahriyar | Ronald Cardenas | Saad Mahamood | Salomey Osei | Samuel Cahyawijaya | Sanja Štajner | Sebastien Montella | Shailza Jolly | Simon Mille | Tahmid Hasan | Tianhao Shen | Tosin Adewumi | Vikas Raunak | Vipul Raheja | Vitaly Nikolaev | Vivian Tsai | Yacine Jernite | Ying Xu | Yisi Sang | Yixin Liu | Yufang Hou
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances. We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other’s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.

2021

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System Description for the CommonGen task with the POINTER model
Anna Shvets
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

In a current experiment we were testing CommonGen dataset for structure-to-text task from GEM living benchmark with the constraint based POINTER model. POINTER represents a hybrid architecture, combining insertion-based and transformer paradigms, predicting the token and the insertion position at the same time. The text is therefore generated gradually in a parallel non-autoregressive manner, given the set of keywords. The pretrained model was fine-tuned on a training split of the CommonGen dataset and the generation result was compared to the validation and challenge splits. The received metrics outputs, which measure lexical equivalence, semantic similarity and diversity, are discussed in details in a present system description.