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Found 20 papers, 3 papers with code
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems
Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on synthetic images.
Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation
We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension.
Entity-based De-noising Modeling for Controllable Dialogue Summarization
Although fine-tuning pre-trained backbones produces fluent and grammatically-correct text in various language generation tasks, factual consistency in abstractive summarization remains challenging.
PromptSum: Parameter-Efficient Controllable Abstractive Summarization
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios.
Unsupervised Summarization Re-ranking
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks.
Taking Quantitative Genomics into the Wild
A key goal in studies of ecology and evolution is understanding the causes of phenotypic diversity in nature.
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks
While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education.
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding.
Exploiting Discourse-Level Segmentation for Extractive Summarization
We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer.
Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes
This task differs from other natural language generation tasks in the following ways: (1) The input is a set of identifiable entities (ICD codes) where the relations between individual entity are not explicitly specified.
Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of Chinese Characters
Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs{'} recursive structures.
Acoustic Characterization of Singaporean Children's English: Comparisons to American and British Counterparts
We investigate English pronunciation patterns in Singaporean children in relation to their American and British counterparts by conducting archetypal analysis on selected vowel pairs.
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension
Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers.
Report of NEWS 2018 Named Entity Transliteration Shared Task
This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia.
Attention-based Semantic Priming for Slot-filling
The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena.
Statistical Machine Transliteration Baselines for NEWS 2018
This paper reports the results of our trans-literation experiments conducted on NEWS 2018 Shared Task dataset.
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
Clustering-based Phonetic Projection in Mismatched Crowdsourcing Channels for Low-resourced ASR
We evaluate our techniques using mismatched transcriptions for Cantonese speech acquired from native English and Mandarin speakers.
