Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

Recent studies in deep learning-based speech separation have proven the superiority of
time-domain approaches to conventional time-frequency-based methods. Unlike the time …

Existing approaches to neural machine translation are typically autoregressive models.
While these models attain state-of-the-art translation quality, they are suffering from low …

We present a generative model to map natural language questions into SQL queries.
Existing neural network based approaches typically generate a SQL query word-by-word …

Most recent studies for relation extraction (RE) leverage the dependency tree of the input
sentence to incorporate syntax-driven contextual information to improve model performance …

In this paper, we describe the team UT-IIS's system and results for the WAT 2017 translation
tasks. We further investigated several tricks including a novel technique for initializing …

Supertagging is conventionally regarded as an important task for combinatory categorial
grammar (CCG) parsing, where effective modeling of contextual information is highly …

This study proposes a new neural machine translation (NMT) model based on the encoder-
decoder model that incorporates named entity (NE) tags of source-language sentences …

We present assertion based question answering (ABQA), an open domain question
answering task that takes a question and a passage as inputs, and outputs a semi-structured …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …