Democratizing access to natural language processing (NLP) technology is
...
Grounding dialogue response generation on external knowledge is proposed...
Query-Focused Meeting Summarization (QFMS) aims to generate a summary of...
The challenge of fairness arises when Automatic Speech Recognition (ASR)...
Instruction-tuned large language models (LLMs) have shown remarkable
gen...
General-purpose language models that can solve various language-domain t...
Conversational models that are generative and open-domain are particular...
The demand for multimodal dialogue systems has been rising in various
do...
This paper proposes a framework for quantitatively evaluating interactiv...
We present NusaCrowd, a collaborative initiative to collect and unite
ex...
Dialogue systems can leverage large pre-trained language models and know...
Developing robust and fair AI systems require datasets with comprehensiv...
Large-scale vision-language pre-trained (VLP) models are prone to halluc...
Many NLP classification tasks, such as sexism/racism detection or toxici...
Closed-book question answering (QA) requires a model to directly answer ...
Generating a short story out of an image is arduous. Unlike image captio...
With the rise of deep learning and intelligent vehicles, the smart assis...
Natural language processing (NLP) has a significant impact on society vi...
Hate speech detection is complex; it relies on commonsense reasoning,
kn...
Considerable advancements have been made in various NLP tasks based on t...
Self-supervised pre-training methods have brought remarkable breakthroug...
Media framing bias can lead to increased political polarization, and thu...
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data a...
Multi-hop question generation (MQG) aims to generate complex questions w...
Code-switching is a speech phenomenon when a speaker switches language d...
Named entity recognition (NER) models generally perform poorly when larg...
The exponential development and application of artificial intelligence
t...
Learning to converse using only a few examples is a great challenge in
c...
General-purpose language models have demonstrated impressive capabilitie...
While the recent advances in deep neural networks (DNN) bring remarkable...
Zero-shot transfer learning for dialogue state tracking (DST) enables us...
Multimodal abstractive summarization (MAS) models that summarize videos
...
Politically sensitive topics are still a challenge for open-domain chatb...
Task-oriented compositional semantic parsing (TCSP) handles complex nest...
Information-seeking dialogue systems, including knowledge identification...
Task-oriented dialogue (ToD) benchmarks provide an important avenue to
m...
Over the past year, research in various domains, including Natural Langu...
The current pandemic has forced people globally to remain in isolation a...
The scarcity of parallel data is a major obstacle for training high-qual...
Query focused summarization (QFS) models aim to generate summaries from
...
This paper introduces QAConv, a new question answering (QA) dataset that...
To diversify and enrich generated dialogue responses, knowledge-grounded...
The data scarcity in low-resource languages has become a bottleneck to
b...
Multimodal affect recognition constitutes an important aspect for enhanc...
Rumors are often associated with newly emerging events, thus, an ability...
A benchmark provides an ecosystem to measure the advancement of models w...
In this paper, we introduce UnifiedM2, a general-purpose misinformation ...
Media bias can lead to increased political polarization, and thus, the n...
Multilingual language models have shown decent performance in multilingu...
State-of-the-art abstractive summarization models generally rely on exte...