Kenton Lee @kentonctlee
Joined January 2015-
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Excited to present Pix2Struct! It's a general-purpose pixel-to-text model that can be finetuned on tasks with visually-situated language, such as UIs, charts, figures, tables, documents, etc. -- arxiv.org/abs/2210.03347 (1/4)
New from Google Research! Retrieval tasks are quite different -- Few-shot adaptation is important! With 8 annotated examples, Promptagator🐊 can figure out the task and make dual encoders outperform all prior MSMARCO trained retrievers in experiments arxiv.org/abs/2209.11755 🧵1/n
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?
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)
Excited to share our new EMNLP paper, “CapWAP: Captioning with a Purpose”! We take a fresh perspective on image captioning by giving it an explicit purpose. In CapWAP, models are trained and evaluated with respect to information that users care about. arxiv.org/abs/2011.04264
Knowledge is not uniformly distributed across languages so an ideal open-retrieval QA model should search and retrieve multilingual resources. We propose a new task 𝐗𝐎𝐑 𝐐𝐀 with a new dataset 𝐗𝐎𝐑-𝐓𝐲𝐃𝐢 QA(40k newly annotated Qs in 7 languages) nlp.cs.washington.edu/xorqa/👇
We're doing a live online Q&A on REALM: Retrieval Augmented Language Model Pre-training at #ICML2020: icml.cc/virtual/2020/p… (Tue Jul 14, 9 AM PDT and again at 8 PM PDT).
Announcing the EfficientQA competition and #NeurIPS2020 workshop, a collaborative effort with @Princeton and @UW that challenges developers to create end-to-end open-domain question answering systems that are small, yet robust. Learn all about it ↓ goo.gle/2AVm3Vg
New paper on representing texts by contextualizing them jointly with textual encyclopedic knowledge (TEK) retrieved dynamically from multiple documents in Wikipedia. Joint work with @kentonctlee, Yi Luan & @toutanova from my internship at @GoogleAI. arxiv.org/abs/2004.12006 (1/4)
Our BERT miniatures were pre-trained directly with MLM loss. They are competitive to more elaborate pre-training strategies involving MLM distillation (arxiv.org/abs/1908.08962). Our models can be fine-tuned for downstream tasks via standard training or end-task distillation.
Efficient BERT models from Google Research, now available at github.com/google-researc…! We hope our 24 BERT models with fewer layers and/or hidden sizes will enable research in resource-constrained institutions and encourage building more compact models. arxiv.org/abs/1908.08962
@kentonctlee @IcePasupat @mchang21 Instead of compressing massive textual knowledge into billions of uninterpretable parameters, REALM exposes the provenance for its representations and decision making. Corollary: we can control what REALM “knows” by hot-swapping the corpus.
New from Google Research! REALM: realm.page.link/paper We pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open!
Shoutout to @JonClarkSeattle from our team in Seattle, who led this project (in collaboration with many other stellar researchers from our group in NYC); it is a rigorously collected dataset for 11 diverse languages for information-seeking QA. Please try it!
Shoutout to @JonClarkSeattle from our team in Seattle, who led this project (in collaboration with many other stellar researchers from our group in NYC); it is a rigorously collected dataset for 11 diverse languages for information-seeking QA. Please try it!
What goes on in the mind of BERT? Using an interactive visualization tool, we uncovered some surprisingly intuitive patterns. Article: medium.com/@JesseVig/deco… Colab: colab.research.google.com/drive/1vlOJ1lh… Github: github.com/jessevig/bertv… Play with it and share what you find! #nlproc
Code and pretrained weights for BERT are out now. Includes scripts to reproduce results. BERT-Base can be fine-tuned on a standard GPU; for BERT-Large, a Cloud TPU is required (as max batch size for 12-16 GB is too small). github.com/google-researc…
New preprint from our team in Seattle. State of the art on everything! arxiv.org/abs/1810.04805
A new era of NLP has just begun a few days ago: large pretraining models (Transformer 24 layers, 1024 dim, 16 heads) + massive compute is all you need. BERT from @GoogleAI: SOTA results on everything arxiv.org/abs/1810.04805. Results on SQuAD are just mind-blowing. Fun time ahead!
the paper behind BERT is now online: arxiv.org/abs/1810.04805 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova arxiv.org/abs/1810.04805
(((ل()(ل() 'yoav))).. @yoavgo
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Percy Liang @percyliang
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Yoav Artzi @yoavartzi
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Ofir Press 🖋 @OfirPress
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Dipanjan Das @dipanjand
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Graham Neubig @gneubig
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Tim Dettmers @Tim_Dettmers
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Wenhu Chen @WenhuChen
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Delip Rao e/σ @deliprao
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camenduru @camenduru
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Pensé FFun @inftyCategory
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Arkil @arkil_patel
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Rong Ching Chang @AnnCC12
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Ekin Akyürek @akyurekekin
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Mogan @IAmMogan
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turing — e/acc @linus_turing
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Richard Diehl Martine.. @richarddm1
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(((ل()(ل() 'yoav))).. @yoavgo
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Akari Asai @AkariAsai
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Percy Liang @percyliang
49K Followers 408 Following Associate Professor in computer science @Stanford @StanfordHAI @StanfordCRFM @StanfordAILab @stanfordnlp | cofounder @togethercompute | Pianist
Kyunghyun Cho @kchonyc
61K Followers 2K Following a combination of a mediocre scientist, a mediocre manager, a mediocre advisor & a mediocre PC at @nyuniversity (@CILVRatNYU) & @genentech (@PrescientDesign).
Yoav Artzi @yoavartzi
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Mike Lewis @ml_perception
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Jacob Andreas @jacobandreas
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Christopher Manning @chrmanning
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Maxwell Forbes @maxforbes
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Nando de Freitas 🏳.. @NandoDF
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Alexis Conneau @alex_conneau
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Michael Roth @microth
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Ves Stoyanov @vesko_st
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296K Followers 6K Following Chief Scientist, Google DeepMind and Google Research. Co-designer/implementor of things like @TensorFlow, MapReduce, Bigtable, Spanner, Gemini .. (he/him)
📍🧵🚨 QA on plots & charts is a complex task requiring sophisticated reasoning - our visual language models struggle with this. LLMs are super strong reasoners - but they only work for text. What do we do? We translate plots & charts to text so LLM can understand!
MATCHA : Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering Outperforms SotA by up to 20% on PlotQA and ChartQA - Transfers well to domains like screenshot, diagrams, and document figures arxiv.org/abs/2212.09662
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding Pretrains a model for VL understanding by pairing a screenshot of a website and the simplified version of the HTML. openreview.net/forum?id=UERcQ…
Headed to #ICML2022 for the first in-person conference since pre-covid! Looking forward to exciting ML discussions with old & new friends Our workshop on Knowledge Retrieval and Language Models knowledge-retrieval-workshop.github.io is on Friday 22nd. Do stop by (or tune in online)! @icmlconf
we need more papers that don't report *any* results, instead focusing on refining shared vocabulary, e.g., what assumptions do different papers implicitly make about what LMs should be able to do? if you're in Punta Cana and interested, I'll be at Bohío Buffet at 7, let's chat!
🚨New pub🚨 How Climate Scenarios Lost Touch With Reality A failure of self-correction in science has compromised climate science’s ability to provide plausible views of our collective future by me & @jritch @ISSUESinST (online 7/26) Read it here now: drive.google.com/file/d/11UMwQ7…
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?
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)
Two great, related papers to this: @adamjfisch, @kentonctlee, @mchang21, @JonClarkSeattle, Barzilay (2020). CapWAP. aclweb.org/anthology/2020… Zhao, Sharma, @TomerLevinboim, Soricut (2019). Informative Image Captioning with External Sources of Information. aclweb.org/anthology/P19-…
Real life example of the fact that words can label the function or purpose of items rather than the things themselves. (The relationship between images and associated language is diverse and highly goal dependent.)
This work was done together with a great set of collaborators: @kentonctlee , @mchang21 , @JonClarkSeattle , @BarzilayRegina. Code available online: github.com/google-researc….
Just read, and really enjoyed, @kentonctlee, @LuhengH, @ml_perception & @LukeZettlemoyer's coreference resolution and semantic role labeling papers from 2017 - if only all DL-for-NLP work was as clearly written and argued (with ablations) as these papers!
Excited to share new work w/ Ming-Wei Chang (@mchang21), Ice Pasupat (@IcePasupat) and Kristina Toutanova (@toutanova)! Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? Paper link: ptshaw.com/nqgt5
Thrilled to share new work! “Retrieval-Augmented Generation for Knowledge-Intensive NLP tasks”. Big gains on Open-Domain QA, with new State-of-the-Art results on NaturalQuestions, CuratedTrec and WebQuestions. check out here: arxiv.org/abs/2005.11401. 1/N
Last summer we had a unique workshop/hackathon on technology for language documentation and revitalization. Here's a report of the various projects developed there, covering universal ASR, dictionary managers, corpus search tools, and social media bots: arxiv.org/pdf/2004.13203…
We will be holding a week-long development workshop on "Language Technology for Language Documentation and Revitalization" in Pittsburgh 8/12-16. Please consider joining if you're interested in building tools to help threatened/endangered languages sites.google.com/view/ltldr/home
Does "When did harry potter and sorcerer's stone movie come out?" look ambiguous? Ambiguity is inherent to open-domain QA. We introduce a new QA task for predicting question-answer pairs that represent different interpretations of the original question. arxiv.org/abs/2004.10645
This was an interesting paper to read. It's well-written, and the method that's used clearly works very well. A few things struck me as I read:
New from Google Research! REALM: realm.page.link/paper We pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open!
@kentonctlee @IcePasupat @mchang21 Bonus result -- head-to-head comparison between two paradigms: implicit knowledge storage (e.g. BERT, GPT2, T5) vs explicit knowledge storage (e.g. REALM).
@kentonctlee @IcePasupat @mchang21 Instead of compressing massive textual knowledge into billions of uninterpretable parameters, REALM exposes the provenance for its representations and decision making. Corollary: we can control what REALM “knows” by hot-swapping the corpus.
Joint work with Kenton Lee (@kentonctlee), Zora Tung, Ice Pasupat (@IcePasupat) and Ming-Wei Chang (@mchang21)
New from Google Research! REALM: realm.page.link/paper We pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open!
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