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Found 11 papers, 6 papers with code
The University of Edinburgh-Uppsala University’s Submission to the WMT 2020 Chat Translation Task
Our baseline systems are transformer-big models that are pre-trained on the WMT’19 News Translation task and fine-tuned on pseudo-in-domain web crawled data and in-domain task data.
Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets
We benchmark metric performance, assess their incremental performance over successive campaigns, and measure their sensitivity to a range of linguistic phenomena.
Interpreting User Requests in the Context of Natural Language Standing Instructions
Users of natural language interfaces, generally powered by Large Language Models (LLMs), often must repeat their preferences each time they make a similar request.
ACES: Translation Accuracy Challenge Sets at WMT 2023
We benchmark the performance of segmentlevel metrics submitted to WMT 2023 using the ACES Challenge Set (Amrhein et al., 2022).
Extrinsic Evaluation of Machine Translation Metrics
We calculate the correlation between the metric's ability to predict a good/bad translation with the success/failure on the final task for the Translate-Test setup.
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue
We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the NLU tasks of intent detection and slot labelling for TOD systems in the multilingual setting.
ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics
As machine translation (MT) metrics improve their correlation with human judgement every year, it is crucial to understand the limitations of such metrics at the segment level.
Ranked #1 on
Machine Translation
on ACES
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking
In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks.
On Incorporating Structural Information to improve Dialogue Response Generation
This requires capturing structural, sequential and semantic information from the conversation context and the background resources.
Towards Exploiting Background Knowledge for Building Conversation Systems
Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them.
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems
("Can you help me in booking a table at this restaurant?").
