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Found 14 papers, 10 papers with code
Learned Incremental Representations for Parsing
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.
Interactive Assignments for Teaching Structured Neural NLP
We present a set of assignments for a graduate-level NLP course.
SMYRF - Efficient Attention using Asymmetric Clustering
We propose a novel type of balanced clustering algorithm to approximate attention.
SMYRF: Efficient Attention using Asymmetric Clustering
We also show that SMYRF can be used interchangeably with dense attention before and after training.
Unsupervised Parsing via Constituency Tests
We propose a method for unsupervised parsing based on the linguistic notion of a constituency test.
Multilingual Alignment of Contextual Word Representations
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
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.
Ranked #2 on
Question Answering
on Quasart-T
Cross-Domain Generalization of Neural Constituency Parsers
Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains?
KERMIT: Generative Insertion-Based Modeling for Sequences
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 $p(y)$.
Ranked #39 on
Machine Translation
on WMT2014 English-German
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence.
Ranked #12 on
Constituency Parsing
on Penn Treebank
Multilingual Constituency Parsing with Self-Attention and Pre-Training
We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions.
Ranked #5 on
Constituency Parsing
on CTB5
Constituency Parsing with a Self-Attentive Encoder
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.
Ranked #8 on
Constituency Parsing
on CTB5
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
The game involves two players: a Teller and a Drawer.
Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space
We present a model for locating regions in space based on natural language descriptions.
