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Adversarial NLI: A New Benchmark for Natural Language Understanding


Nov 01 2019 Douwe Kiela

Excited (in my 1st tweet ever!) to announce Adversarial NLI: a new large-scale benchmark dataset for NLU, and a challenge to the community. Great job by @EasonNie, together with @adinamwilliams @em_dinan @mohitban47 and @jaseweston.
5 replies, 304 likes

Nov 01 2019 Gary Marcus

"A growing body of evidence shows that state-of-the-art models learn to exploit spurious statistical patterns in datasets .. instead of learning meaning in the flexible and generalizable way that humans do" -- Adversarial NLI: A New Benchmark for NLU
9 replies, 209 likes

Nov 01 2019 Mohit Bansal

Exciting work by @EasonNie (+@adinamwilliams @em_dinan @jaseweston @douwekiela)! Adversarial NLI, a large dataset collected via a multi-round adversarial (weakness-finding) human-&-model-in-the-loop process; allows moving/lifelong-learning target for NLU😀
1 replies, 48 likes

Nov 01 2019 Kyunghyun Cho

ahahahaha "there is something rotten in the state of the art"
0 replies, 22 likes

Nov 01 2019 Grady Booch

I feel a chill in the air.
2 replies, 20 likes

Nov 01 2019 Matt Gardner

Glad to see this actually done; I've wanted to do something similar for reading comprehension, but it's hard to get right. Nice work! Question, though: the paper says that NLI is "arguably the most canonical task in NLU". Is it? Should it be? Why or why not?
1 replies, 17 likes

Nov 01 2019 MUNOZRICK

Most #AI tech seems more akin to muscle memory or capturing instinctive behavior, rather than actual learning or intelligence. More cerebellum than prefrontal cortex
0 replies, 12 likes

Nov 01 2019 claps for anyone 👏👏👏

I would hope everyone knows this already by now.
1 replies, 8 likes

Nov 01 2019 Beth Carey

Meaning is represented for machines by #Patom Theory + #RRG=NLU. Once machines have meaning represented, applications like #chatbots and #digitalassistants can leverage context in conversation - common sense and reasoning are by products.
0 replies, 5 likes

Feb 07 2020 Jerome Pesenti

Why we should be skeptical of claims of near-human performance in NLP based on traditional benchmarks by @benbenhh. NLP made huge progress in the past two years, but human like pretensions will require new evaluations. My bet is on
0 replies, 1 likes

Feb 24 2020 Alex Hamilton

@anthonyncutler The quote is itself taken from a Facebook AI paper, which quotes a number of other papers. You can read it here:
1 replies, 1 likes

Nov 02 2019 arXiv CS-CL

Adversarial NLI: A New Benchmark for Natural Language Understanding
0 replies, 1 likes