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Found 27 papers, 18 papers with code
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem.
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition.
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation.
A Transition-based System for Universal Dependency Parsing
This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
Exploring Recombination for Efficient Decoding of Neural Machine Translation
In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations.
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
Different languages might have different word orders.
Choosing Transfer Languages for Cross-Lingual Learning
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
The parameters of source model and target model are softly shared through a regularized log likelihood objective.
An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing
In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017).
SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation
In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.
Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages
We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages.
Neural Polysynthetic Language Modelling
In the literature, languages like Finnish or Turkish are held up as extreme examples of complexity that challenge common modelling assumptions.
A Two-Step Approach for Implicit Event Argument Detection
It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction
Recently, pre-training contextualized encoders with language model (LM) objectives has been shown an effective semi-supervised method for structured prediction.
Incorporating a Local Translation Mechanism into Non-autoregressive Translation
In this work, we introduce a novel local autoregressive translation (LAT) mechanism into non-autoregressive translation (NAT) models so as to capture local dependencies among tar-get outputs.
Ranked #2 on
Machine Translation
on WMT2016 English-Romanian
Towards More Efficient Insertion Transformer with Fractional Positional Encoding
Nevertheless, due to the incompatibility between absolute positional encoding and insertion-based generation schemes, it needs to refresh the encoding of every token in the generated partial hypothesis at each step, which could be costly.
A Survey of Active Learning for Natural Language Processing
In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP).
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training
To address this challenge, we adopt an error estimator to adaptively decide the partial selection ratio according to the current model's capability.
Reasons to Reject? Aligning Language Models with Judgments
As humans, we consistently engage in interactions with our peers and receive feedback in the form of natural language.
A Thorough Examination of Decoding Methods in the Era of LLMs
Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers.
Self-playing Adversarial Language Game Enhances LLM Reasoning
Hence, we are curious about whether LLMs' reasoning ability can be further enhanced by Self-Play in this Adversarial language Game (SPAG).
On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling
Although recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL training data are not abundant.
Comparing Span Extraction Methods for Semantic Role Labeling
In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL).
