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Adversarial NLI: A New Benchmark for Natural Language Understanding


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, 317 likes

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, 210 likes

Douwe Kiela: Just updated the ANLI paper with the #acl2020nlp camera ready: Lots of extra stuff: more analysis on the value of dynamic adversarial data collection, details on annotators and more discussion. (1/2)

1 replies, 81 likes

Mohit Bansal (@🏡): It's live!🥳 Excited to continue on the @DynabenchAI journey (w. @facebookai @ucl_nlp @stanfordnlp) for human-model-in-loop benchmarks, & extend AdvNLI ( to more diverse rounds (r4 coming soon) +related tasks! cc @EasonNie @uncnlp

0 replies, 68 likes

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

Kyunghyun Cho: ahahahaha "there is something rotten in the state of the art"

0 replies, 22 likes

Adina Williams: Super stoked that Dynabench is live! It's been a pleasure to be part of this all-star cast! Check it out and try your hand at writing examples here: More info: ANLI paper: #NLProc #ML #MachineLearning #AI

1 replies, 21 likes

Grady Booch: I feel a chill in the air.

2 replies, 20 likes

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

1 replies, 18 likes

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

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

claps for anyone 👏👏👏: I would hope everyone knows this already by now.

1 replies, 8 likes

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

arXiv CS-CL: Adversarial NLI: A New Benchmark for Natural Language Understanding

0 replies, 1 likes

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


Found on Nov 01 2019 at

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