Search Results for author: Nikita Kitaev

Found 14 papers, 10 papers with code

Learned Incremental Representations for Parsing

1 code implementation ACL 2022 Nikita Kitaev, Thomas Lu, Dan Klein

We present an incremental syntactic representation that consists of assigning a single discrete label to each word in a sentence, where the label is predicted using strictly incremental processing of a prefix of the sentence, and the sequence of labels for a sentence fully determines a parse tree.

Constituency Parsing Sentence

SMYRF: Efficient Attention using Asymmetric Clustering

1 code implementation11 Oct 2020 Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis

We also show that SMYRF can be used interchangeably with dense attention before and after training.

16k Clustering

Unsupervised Parsing via Constituency Tests

no code implementations EMNLP 2020 Steven Cao, Nikita Kitaev, Dan Klein

We propose a method for unsupervised parsing based on the linguistic notion of a constituency test.


Multilingual Alignment of Contextual Word Representations

no code implementations ICLR 2020 Steven Cao, Nikita Kitaev, Dan Klein

We propose procedures for evaluating and strengthening contextual embedding alignment and show that they are useful in analyzing and improving multilingual BERT.


Reformer: The Efficient Transformer

16 code implementations ICLR 2020 Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya

Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences.

D4RL Image Generation +3

Cross-Domain Generalization of Neural Constituency Parsers

1 code implementation ACL 2019 Daniel Fried, Nikita Kitaev, Dan Klein

Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains?

Constituency Parsing Domain Generalization

KERMIT: Generative Insertion-Based Modeling for Sequences

no code implementations4 Jun 2019 William Chan, Nikita Kitaev, Kelvin Guu, Mitchell Stern, Jakob Uszkoreit

During training, one can feed KERMIT paired data $(x, y)$ to learn the joint distribution $p(x, y)$, and optionally mix in unpaired data $x$ or $y$ to refine the marginals $p(x)$ or $p(y)$.

Machine Translation Question Answering +2

Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference

2 code implementations ACL 2020 Nikita Kitaev, Dan Klein

We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence.

Constituency Parsing Sentence

Multilingual Constituency Parsing with Self-Attention and Pre-Training

4 code implementations ACL 2019 Nikita Kitaev, Steven Cao, Dan Klein

We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions.

Constituency Parsing Unsupervised Pre-training

Constituency Parsing with a Self-Attentive Encoder

4 code implementations ACL 2018 Nikita Kitaev, Dan Klein

We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser.

Constituency Parsing Sentence

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