Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

In this paper, we present a conceptually simple while empirically powerful framework for
abstractive summarization, SimCLS, which can bridge the gap between the learning …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue
systems. However, the inevitable errors from automatic speech recognition (ASR) usually …

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for
collaborative filtering (CF). To improve the representation quality over limited labeled data …

How can we learn unified representations for spoken utterances and their written text?
Learning similar representations for semantically similar speech and text is important for …

Zero-shot stance detection (ZSSD) is challenging as it requires detecting the stance of
previously unseen targets during the inference stage. Being able to detect the target-related …

In recent years, considerable research has been dedicated to the application of neural
models in the field of natural language generation (NLG). The primary objective is to …

Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …

Current state-of-the-art cross-lingual summarization models employ multi-task learning
paradigm, which works on a shared vocabulary module and relies on the self-attention …