Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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Innehåll tillhandahållet av NLP Highlights and Allen Institute for Artificial Intelligence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av NLP Highlights and Allen Institute for Artificial Intelligence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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122 - Statutory Reasoning in Tax Law, with Nils Holzenberger
MP3•Episod hem
Manage episode 277252296 series 1452120
Innehåll tillhandahållet av NLP Highlights and Allen Institute for Artificial Intelligence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av NLP Highlights and Allen Institute for Artificial Intelligence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
We invited Nils Holzenberger, a PhD student at JHU to talk about a dataset involving statutory reasoning in tax law Holzenberger et al. released recently. This dataset includes difficult textual entailment and question answering problems that involve reasoning about how sections in tax law are applicable to specific cases. They also released a Prolog solver that fully solves the problems, and show that learned models using dense representations of text perform poorly. We discussed why this is the case, and how one can train models to solve these challenges. Project webpage: https://nlp.jhu.edu/law/
…
continue reading
145 episoder
MP3•Episod hem
Manage episode 277252296 series 1452120
Innehåll tillhandahållet av NLP Highlights and Allen Institute for Artificial Intelligence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av NLP Highlights and Allen Institute for Artificial Intelligence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
We invited Nils Holzenberger, a PhD student at JHU to talk about a dataset involving statutory reasoning in tax law Holzenberger et al. released recently. This dataset includes difficult textual entailment and question answering problems that involve reasoning about how sections in tax law are applicable to specific cases. They also released a Prolog solver that fully solves the problems, and show that learned models using dense representations of text perform poorly. We discussed why this is the case, and how one can train models to solve these challenges. Project webpage: https://nlp.jhu.edu/law/
…
continue reading
145 episoder
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