Search Results for author: Nico Daheim

Found 13 papers, 9 papers with code

Variational Learning is Effective for Large Deep Networks

1 code implementation27 Feb 2024 Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff

We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.

Model Merging by Uncertainty-Based Gradient Matching

no code implementations19 Oct 2023 Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail?

Uncertainty in Natural Language Generation: From Theory to Applications

no code implementations28 Jul 2023 Joris Baan, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, Wilker Aziz

Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like summarisation or translation, but also serve as a natural language interface to a variety of applications.

Active Learning Text Generation

MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems

1 code implementation23 May 2023 Jakub Macina, Nico Daheim, Sankalan Pal Chowdhury, Tanmay Sinha, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan

While automatic dialogue tutors hold great potential in making education personalized and more accessible, research on such systems has been hampered by a lack of sufficiently large and high-quality datasets.

Language Modelling Large Language Model +1

Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10

no code implementations14 Apr 2023 David Thulke, Nico Daheim, Christian Dugast, Hermann Ney

This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10).

Automatic Speech Recognition Data Augmentation +2

Elastic Weight Removal for Faithful and Abstractive Dialogue Generation

1 code implementation30 Mar 2023 Nico Daheim, Nouha Dziri, Mrinmaya Sachan, Iryna Gurevych, Edoardo M. Ponti

We evaluate our method -- using different variants of Flan-T5 as a backbone language model -- on multiple datasets for information-seeking dialogue generation and compare our method with state-of-the-art techniques for faithfulness, such as CTRL, Quark, DExperts, and Noisy Channel reranking.

Dialogue Generation Language Modelling

Opportunities and Challenges in Neural Dialog Tutoring

1 code implementation24 Jan 2023 Jakub Macina, Nico Daheim, Lingzhi Wang, Tanmay Sinha, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan

Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors.

Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model

1 code implementation31 Oct 2022 Nico Daheim, David Thulke, Christian Dugast, Hermann Ney

In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes theorem.

Response Generation

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model

1 code implementation16 Dec 2021 David Thulke, Nico Daheim, Christian Dugast, Hermann Ney

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".

Data Augmentation Task 2

Cascaded Span Extraction and Response Generation for Document-Grounded Dialog

1 code implementation ACL (dialdoc) 2021 Nico Daheim, David Thulke, Christian Dugast, Hermann Ney

For the second subtask, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document.

Response Generation valid

Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog

1 code implementation9 Feb 2021 David Thulke, Nico Daheim, Christian Dugast, Hermann Ney

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".

Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.