Large language models (LLMs) have demonstrated impressive capabilities i...
Large language models (LLM) have demonstrated their abilities to solve
v...
Cross-lingual text classification leverages text classifiers trained in ...
Pre-trained speech encoders have been central to pushing state-of-the-ar...
Textual scene graph parsing has become increasingly important in various...
Norms, which are culturally accepted guidelines for behaviours, can be
i...
Multilingual semantic parsing aims to leverage the knowledge from the
hi...
Nowadays, voice assistants help users complete tasks on the smartphone w...
Text-based games (TGs) are language-based interactive environments for
r...
Acquiring new knowledge without forgetting what has been learned in a
se...
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow ...
Dialogue systems have been widely applied in many scenarios and are now ...
Knowledge graphs (KGs), as a structured form of knowledge representation...
In very recent years more attention has been placed on probing the role ...
In this paper, we conduct the first study on spurious correlations for
o...
Existing work in document-level neural machine translation commonly
conc...
Current multilingual semantic parsing (MSP) datasets are almost all coll...
Semantic parsing is a technique aimed at constructing a structured
repre...
Negotiation is one of the crucial abilities in human communication, and ...
Existing object detection methods are bounded in a fixed-set vocabulary ...
Multi-hop reading comprehension requires not only the ability to reason ...
Pre-trained speech Transformers have facilitated great success across va...
Previous works mostly focus on either multilingual or multi-domain aspec...
Relation extraction typically aims to extract semantic relationships bet...
Pre-trained speech Transformers in speech translation (ST) have facilita...
Answering complex questions that require multi-step multi-type reasoning...
Interpretable machine learning seeks to understand the reasoning process...
Interpretable machine learning offers insights into what factors drive a...
End-to-end speech-to-text translation models are often initialized with
...
In this paper, we propose a variational autoencoder with disentanglement...
Automatic Cognate Detection (ACD) is a challenging task which has been
u...
Cognates are present in multiple variants of the same text across differ...
Cognates are variants of the same lexical form across different language...
Automatic detection of cognates helps downstream NLP tasks of Machine
Tr...
Differentiable Architecture Search (DARTS) has received massive attentio...
Medical Visual Question Answering (VQA) is a combination of medical
arti...
Vision-and-Language Navigation (VLN) is a task that an agent is required...
Multilingual Neural Machine Translation (MNMT) trains a single NMT model...
Most existing simultaneous machine translation (SiMT) systems are traine...
This paper investigates continual learning for semantic parsing. In this...
This paper considers the unsupervised domain adaptation problem for neur...
Numerical reasoning skills are essential for complex question answering ...
Machine-learning-as-a-service (MLaaS) has attracted millions of users to...
With leveraging the weight-sharing and continuous relaxation to enable
g...
Semi-Supervised Learning (SSL) has seen success in many application doma...
Event detection (ED) aims at detecting event trigger words in sentences ...
Commonsense reasoning aims to incorporate sets of commonsense facts,
ret...
In this work, we investigate the problems of semantic parsing in a few-s...
Current approaches which are mainly based on the extraction of low-level...
This study develops a pattern recognition method that identifies pattern...