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Found 7 papers, 2 papers with code
MiSS: An Assistant for Multi-Style Simultaneous Translation
With this system, we aim to provide a complete translation experience for machine translation users.
Cross-lingual Transferring of Pre-trained Contextualized Language Models
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
Grammatical Error Correction as GAN-like Sequence Labeling
In Grammatical Error Correction (GEC), sequence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are passed to the model for multiple rounds of correction, which exposes the model to sentences with progressively fewer errors at each round.
Head-driven Phrase Structure Parsing in O($n^3$) Time Complexity
Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, Head-driven Phrase Structure Grammar (HPSG).
High-order Semantic Role Labeling
Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships.
Global Greedy Dependency Parsing
Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models.
Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank
In this paper, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role, providing a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation.
