Kristina Toutanova

35 posts

Kristina Toutanova

Kristina Toutanova

@toutanova

Katılım Ekim 2018
213 Takip Edilen906 Takipçiler
Kristina Toutanova retweetledi
Pete Shaw
Pete Shaw@ptshaw2·
Excited to share a new paper that aims to narrow the conceptual gap between the idealized notion of Kolmogorov complexity and practical complexity measures for neural networks.
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INSAIT Institute
INSAIT Institute@INSAITinstitute·
‼️ We are delighted to release the paper describing the method used to create the BgGPT series of models (bggpt.ai). Method is applicable and can be used to fine-tune any base model to obtain new skills (e.g., BG) without forgetting old ones (e.g., math, EN).
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Mandar Joshi
Mandar Joshi@mandarjoshi_·
Excited to present Pix2Act! An agent that can interact with GUIs using the same conceptual interface that humans commonly use — via pixel-based screenshots and generic keyboard and mouse actions -- arxiv.org/abs/2306.00245 (1/4)
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Kristina Toutanova retweetledi
Linlu Qiu
Linlu Qiu@linluqiu·
Is scale all you need for semantic parsing? We present a systematic study of scaling curves measuring compositional generalization in semantic parsing across model types and task adaptation techniques: arxiv.org/abs/2205.12253 at #EMNLP2022 (1/n)
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Bhuwan Dhingra
Bhuwan Dhingra@bhuwandhingra·
🤔 When does a factoid question need a *long* answer? 🤖 "Long" could mean multiple things: either you ask for a city with a very long name or … Read Ivan Stelmakh's internship paper to get the second part of the answer! arxiv.org/abs/2204.06092
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Pete Shaw
Pete Shaw@ptshaw2·
Our group is looking for a student researcher to work on measuring and improving the compositional generalization capabilities of neural networks. Come work with me, Kristina Toutanova (@toutanova), and others across Google Research! (1/2)
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Linlu Qiu
Linlu Qiu@linluqiu·
“Improving Compositional Generalization with Latent Structure and Data Augmentation” arxiv.org/abs/2112.07610. Can we do better than good-enough compositional data augmentation? We present a data recombination method using a model called Compositional Structure Learner (CSL).
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Julia Turc
Julia Turc@juliarturc·
New from Google Research: arxiv.org/abs/2106.16171 To build multilingual NLP systems, a successful recipe is to pre-train on a multilingual corpus, and then fine-tune on labeled data in a single transfer language -- usually English. But is English best?
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NAACL HLT 2027
NAACL HLT 2027@naaclmeeting·
If you were waiting until after the EMNLP deadline to register for NAACL, now is the time -- early registration ends tomorrow May 20.
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NAACL HLT 2027
NAACL HLT 2027@naaclmeeting·
You can help determine an effective format for the virtual NAACL 2021! You can provide quick feedback on options or volunteer to serve on the virtual infrastructure chairing committee via this form forms.gle/91EfYUcj7Yy5jS…
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Jonathan Clark
Jonathan Clark@JonClarkSeattle·
New from Google Research: CANINE, a pre-trained tokenization-free language encoder. This frees us from a variety of pitfalls associated with tokenization, but also improves quality on TyDi QA, a multilingual question answering benchmark. arxiv.org/abs/2103.06874
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Kelvin Guu
Kelvin Guu@kelvin_guu·
New from Google Research! arxiv.org/abs/2102.01335 Show examples from the distribution you want, and our example extrapolator (Ex2) generates new examples from the same distribution. We use Ex2 for data augmentation, improving over SOTA methods on multiple NLP benchmarks! (1/3)
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Sebastian Gehrmann
Sebastian Gehrmann@sebgehr·
Introducing 💎GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. We are organizing shared tasks for our ACL 2021 workshop - Please consider participating! Website: gem-benchmark.com Paper: arxiv.org/abs/2102.01672 #NLProc 🧵1/X
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NAACL HLT 2027
NAACL HLT 2027@naaclmeeting·
As noted in the CFP, #NAACL2021 is incorporating ethical considerations in the review process. For more information on what that will look like, please see our Ethics FAQ for authors: 2021.naacl.org/ethics/faq/
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