Search Results for author:
Found 11 papers, 8 papers with code
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
To increase the accessibility of our model to non-English speakers, we propose to make use of cascaded machine translation and show that this approach can perform comparably to natively multilingual models while being easier to scale to a large number of languages.
Exploring Spoken Named Entity Recognition: A Cross-Lingual Perspective
Recent advancements in Named Entity Recognition (NER) have significantly improved the identification of entities in textual data.
Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10
This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10).
Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Token
We show: (1) [MASK]s can indeed be appended at a later layer, being disentangled from the word embedding; (2) The gathering of contextualized information from unmasked tokens can be conducted with a few layers.
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model
In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes theorem.
Does Joint Training Really Help Cascaded Speech Translation?
Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model
This paper summarizes our submission to Task 2 of the second track of the 10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations".
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition
Data processing is an important step in various natural language processing tasks.
Cascaded Span Extraction and Response Generation for Document-Grounded Dialog
For the second subtask, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document.
On Sampling-Based Training Criteria for Neural Language Modeling
As the vocabulary size of modern word-based language models becomes ever larger, many sampling-based training criteria are proposed and investigated.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog
This paper summarizes our work on the first track of the ninth Dialog System Technology Challenge (DSTC 9), "Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access".
