Artificial Intelligence has suddenly gone from the fringes of science to being everywhere. So how did we get here? And where's this all heading? In this new series of Science Friction, we're finding out.
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110 - Natural Questions, with Tom Kwiatkowski and Michael Collins
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Konten disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh NLP Highlights and Allen Institute for Artificial Intelligence atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
In this episode, Tom Kwiatkowski and Michael Collins talk about Natural Questions, a benchmark for question answering research. We discuss how the dataset was collected to reflect naturally-occurring questions, the criteria used for identifying short and long answers, how this dataset differs from other QA datasets, and how easy it might be to game the benchmark with superficial processing of the text. We also contrast the holistic design in Natural Questions to deliberately targeting specific linguistic phenomena of interest when building a QA dataset. Dataset: https://ai.google.com/research/NaturalQuestions Paper: https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00276
…
continue reading
145 episode
MP3•Beranda episode
Manage episode 258003825 series 1452120
Konten disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh NLP Highlights and Allen Institute for Artificial Intelligence atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
In this episode, Tom Kwiatkowski and Michael Collins talk about Natural Questions, a benchmark for question answering research. We discuss how the dataset was collected to reflect naturally-occurring questions, the criteria used for identifying short and long answers, how this dataset differs from other QA datasets, and how easy it might be to game the benchmark with superficial processing of the text. We also contrast the holistic design in Natural Questions to deliberately targeting specific linguistic phenomena of interest when building a QA dataset. Dataset: https://ai.google.com/research/NaturalQuestions Paper: https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00276
…
continue reading
145 episode
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