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Found 11 papers, 2 papers with code
The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation
This paper describes the University of Maryland’s submissions to the WMT20 Shared Task on Chat Translation.
Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion
SD Sampling uses text-to-image Stable Diffusion to generate multiple images from the given phrase, increasing the likelihood that a subset of images match the one that paired with the text.
Time-Aware Ancient Chinese Text Translation and Inference
In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text.
MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models
However, most of these datasets are not designed for evaluating sense embeddings.
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Numerals that contain much information in financial documents are crucial for financial decision making.
Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language
For more than 91{\%} of the cases, our system can propose at least one acceptable correction within a list of five candidates.
GenSense: A Generalized Sense Retrofitting Model
In the experiment, we show that the generalized model can outperform previous approaches in three types of experiment: semantic relatedness, contextual word similarity and semantic difference.
A Chinese Writing Correction System for Learning Chinese as a Foreign Language
We present a Chinese writing correction system for learning Chinese as a foreign language.
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge
This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task.
Detection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM
Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners.
Detecting Word Usage Errors in Chinese Sentences for Learning Chinese as a Foreign Language
By utilizing certain combination of the sets of features, we can construct a system that favors precision or recall.
