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Found 14 papers, 8 papers with code
Effective Neural Solution for Multi-Criteria Word Segmentation
We present a simple yet elegant solution to train a single joint model on multi-criteria corpora for Chinese Word Segmentation (CWS).
Dual Long Short-Term Memory Networks for Sub-Character Representation Learning
To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example.
Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT
This paper presents new state-of-the-art models for three tasks, part-of-speech tagging, syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding framework known as BERT.
Ten-year Survival Prediction for Breast Cancer Patients
This report assesses different machine learning approaches to 10-year survival prediction of breast cancer patients.
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar.
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal Dependency Parsing
Our results show that models using the multilingual encoder outperform ones using the language specific encoders for most languages.
Handwritten Character Recognition from Wearable Passive RFID
The data is collected sequentially, such that we record both the stroke order and the resulting bitmap.
Levi Graph AMR Parser using Heterogeneous Attention
Coupled with biaffine decoders, transformers have been effectively adapted to text-to-graph transduction and achieved state-of-the-art performance on AMR parsing.
Ranked #18 on
AMR Parsing
on LDC2017T10
ELIT: Emory Language and Information Toolkit
We introduce ELIT, the Emory Language and Information Toolkit, which is a comprehensive NLP framework providing transformer-based end-to-end models for core tasks with a special focus on memory efficiency while maintaining state-of-the-art accuracy and speed.
The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders
Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency while a question still remains whether or not it would perform as well on tasks that are distinct in nature.
An Approach to Inference-Driven Dialogue Management within a Social Chatbot
In the third and final stage, our bot selects a small subset of predicates and translates them into an English response.
DFEE: Interactive DataFlow Execution and Evaluation Kit
DataFlow has been emerging as a new paradigm for building task-oriented chatbots due to its expressive semantic representations of the dialogue tasks.
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing
Sequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks.
Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability
Despite its strengths, AMR is not easily applied to languages or domains without predefined semantic frames, and its use of numbered arguments results in semantic role labels, which are not directly interpretable and are semantically overloaded for parsers.
