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Found 10 papers, 3 papers with code
Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers
Previous studies have shown that the Abstract Meaning Representation (AMR) can improve Neural Machine Translation (NMT).
Competence-Level Prediction and Resume \& Job Description Matching Using Context-Aware Transformer Models
Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job (T2).
Future Language Modeling from Temporal Document History
While there are many automated systems for predicting future numerical data, such as weather, stock prices, and demand for products, there is relatively little work in automatically predicting textual data.
Task Contamination: Language Models May Not Be Few-Shot Anymore
Large language models (LLMs) offer impressive performance in various zero-shot and few-shot tasks.
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models
This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates.
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media
We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions.
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue.
Ranked #3 on
Question Answering
on FriendsQA
Ten-year Survival Prediction for Breast Cancer Patients
This report assesses different machine learning approaches to 10-year survival prediction of breast cancer patients.
Design and Challenges of Cloze-Style Reading Comprehension Tasks on Multiparty Dialogue
According to our analysis, replacing the random data split with a chronological data split reduces test accuracy on previous single-variable passage completion task from 72\% to 34\%, that leaves much more room to improve.
Challenging On Car Racing Problem from OpenAI gym
This project challenges the car racing problem from OpenAI gym environment.
