Google at EMNLP 2018
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Posted by Manaal Faruqui, Senior Research Scientist and Emily Pitler, Staff Research Scientist, Google AI Language
This week, the annual conference onEmpirical Methods in Natural Language Processing (EMNLP 2018) will be held in Brussels, Belgium. Google will have a strong presence at EMNLP with several of our researchers presenting their research on a diverse set of topics, including language identification, segmentation, semantic parsing and question answering, additionally serving in various levels of organization in the conference. Googlers will also be presenting their papers and participating in the co-located Conference onComputational Natural Language Learning (CoNLL 2018) shared task on multilingual parsing.
In addition to this involvement, we are sharing several new datasets with the academic community that are released with papers published at EMNLP, with the goal of accelerating progress in empiricalnatural language processing (NLP). These releases are designed to help account for mismatches between the datasets a machine learning model is trained and tested on, and the inputs an NLP system would be asked to handle “in the wild”. All of the datasets we are releasing include realistic, naturally occurring text, and fall into two main categories: 1) challenge sets for well-studied core NLP tasks (part-of-speech tagging, coreference) and 2) datasets to encourage new directions of research on meaning preservation under rephrasings/edits (query well-formedness, split-and-rephrase, atomic edits):
- Noun-Verb Ambiguity in POS Tagging Dataset : English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite high accuracies on standard datasets. For example: in “Mark which area you want to distress ” several state-of-the-art taggers annotate “Mark ” as a noun instead of a verb. We release a new dataset of over 30,000 naturally occurring non-trivial annotated examples of noun-verb ambiguity. Taggers previously indistinguishable from each other have accuracies ranging from 57% to 75% accuracy on this challenge set.
- Query Wellformedness Dataset : Web search queries are usually “word-salad” style queries with little resemblance to natural language questions (“barack obama height ” as opposed to “What is the height of Barack Obama? ”). Differentiating a natural language question from a query is of importance to several applications include dialogue. We annotate and release 25,100 queries from the open-sourceParalex corpus with ratings on how close they are to well-formed natural language questions.
- WikiSplit: Split and Rephrase Dataset Extracted from Wikipedia Edits : We extract examples of sentence splits from Wikipedia edits where one sentence gets split into two sentences that together preserve the original meaning of the sentence (E.g. “Street Rod is the first in a series of two games released for the PC and Commodore 64 in 1989. ” is split into “Street Rod is the first in a series of two games. ” and “It was released for the PC and Commodore 64 in 1989. ”) The released corpus contains one million sentence splits with a vocabulary of more than 600,000 words.
- WikiAtomicEdits: A Multilingual Corpus of Atomic Wikipedia Edits : Information about how people edit language in Wikipedia can be used to understand the structure of language itself. We pay particular attention to two atomic edits: insertions and deletions that consist of a single contiguous span of text. We extract around 43 million such edits in 8 languages and show that they provide valuable information about entailment and discourse. For example, insertion of “in 1949 ” adds a prepositional phrase to the sentence “She died there after a long illness ” resulting in “She died there in 1949 after a long illness ”.
These datasets join the others that Google has recently released, such asConceptual Captions andGAP Coreference Resolution in addition to ourpast contributions .
Below is a full list of Google’s involvement and publications being presented at EMNLP and CoNLL (Googlers highlighted inblue ). We are particularly happy to announce that the paper “Linguistically-Informed Self-Attention for Semantic Role Labeling ” was awarded one of the twoBest Long Paper awards . This work was done by our 2017 intern Emma Strubell, Googlers Daniel Andor, David Weiss and Google Faculty Advisor Andrew McCallum. We congratulate these authors, and all other researchers who are presenting their work at the conference.
Area Chairs Include:
Ming-Wei Chang , Marius Pasca , Slav Petrov , Emily Pitler , Meg Mitchell , Taro Watanabe
EMNLP Publications
A Challenge Set and Methods for Noun-Verb Ambiguity
Ali Elkahky , Kellie Webster , Daniel Andor , Emily Pitler
A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
Yuan Zhang , Jason Riesa , Daniel Gillick , Anton Bakalov , Jason Baldridge , David Weiss
AirDialogue: An Environment for Goal-Oriented Dialogue Research
Wei Wei , Quoc Le , Andrew Dai , Jia Li
Content Explorer: Recommending Novel Entities for a Document Writer
Michal Lukasik , Richard Zens
Deep Relevance Ranking using Enhanced Document-Query Interactions
Ryan McDonald , George Brokos , Ion Androutsopoulos
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio,William Cohen , Ruslan Salakhutdinov, Christopher D. Manning
Identifying Well-formed Natural Language Questions
Manaal Faruqui , Dipanjan Das
Learning To Split and Rephrase From Wikipedia Edit History
Jan A. Botha , Manaal Faruqui , John Alex , Jason Baldridge , Dipanjan Das
Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga,Daniel Andor , David Weiss , Andrew McCallum
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov,William Cohen
Noise Contrastive Estimation for Conditional Models: Consistency and Statistical Efficiency
Zhuang Ma,Michael Collins
Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification
Kelsey Ball,Dan Garrette
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Minjoon Seo,Tom Kwiatkowski, Ankur P. Parikh , Ali Farhadi, Hannaneh Hajishirzi
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Dipendra Misra,Ming-Wei Chang , Xiaodong He, Wen-tau Yih
Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry , George Foster , Ankur Bapna , Orhan Firat , Wolfgang Macherey
Self-governing neural networks for on-device short text classification
Sujith Ravi , Zornitsa Kozareva
Semi-Supervised Sequence Modeling with Cross-View Training
Kevin Clark,Minh-Thang Luong , Christopher D. Manning,Quoc Le
State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Ji Ma , Kuzman Ganchev , David Weiss
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Da Tang, Xiujun Li, Jianfeng Gao,Chong Wang ,Lihong Li , Tony Jebara
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Xinyi Wang,Hieu Pham , Zihang Dai, Graham Neubig
The Importance of Generation Order in Language Modeling
Nicolas Ford , Daniel Duckworth , Mohammad Norouzi , George Dahl
Training Deeper Neural Machine Translation Models with Transparent Attention
Ankur Bapna , Mia Chen , Orhan Firat , Yuan Cao , Yonghui Wu
Understanding Back-Translation at Scale
Sergey Edunov, Myle Ott, Michael Auli,David Grangier
Unsupervised Natural Language Generation with Denoising Autoencoders
Markus Freitag ,Scott Roy
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
Manaal Faruqui , Ellie Pavlick , Ian Tenney , Dipanjan Das
WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli,Nithum Thain , Jeffery Sorensen , Lucas Dixon
EMNLP Demos
Taku Kudo, John Richardson
Universal Sentence Encoder for English
Daniel Cer , Yinfei Yang , Sheng-yi Kong , Nan Hua , Nicole Limtiaco , Rhomni St. John , Noah Constant , Mario Guajardo-Cespedes , Steve Yuan , Chris Tar , Brian Strope , Ray Kurzweil
CoNLL Shared Task
Multilingual Parsing from Raw Text to Universal Dependencies
Slav Petrov , co-organizer
Universal Dependency Parsing with Multi-Treebank Models
Aaron Smith,Bernd Bohnet , Miryam de Lhoneux, Joakim Nivre, Yan Shao, Sara Stymne
(Winner of theUniversal POS Tagging andMorphological Tagging subtasks, using theopen-sourced Meta-BiLSTM tagger )
CoNLL Publication
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Katharina Kann,Sascha Rothe ,Katja Filippova
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