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Found 16 papers, 6 papers with code
Searching for COMETINHO: The Little Metric That Could
In recent years, several neural fine-tuned machine translation evaluation metrics such as COMET and BLEURT have been proposed.
xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection
Widely used learned metrics for machine translation evaluation, such as COMET and BLEURT, estimate the quality of a translation hypothesis by providing a single sentence-level score.
Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task
Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2).
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations
Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Large Pre-trained Language Models, such as BERT or RoBERTa.
Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues
State of the art models in intent induction require annotated datasets.
Enhancing Portuguese Sign Language Animation with Dynamic Timing and Mouthing
Current signing avatars are often described as unnatural as they cannot accurately reproduce all the subtleties of synchronized body behaviors of a human signer.
The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics
Neural metrics for machine translation evaluation, such as COMET, exhibit significant improvements in their correlation with human judgments, as compared to traditional metrics based on lexical overlap, such as BLEU.
Using Implicit Feedback to Improve Question Generation
Each generated question, after being corrected by the user, is used as a new seed in the next iteration, so more patterns are created each time.
CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE).
Towards a Sentiment-Aware Conversational Agent
In this paper, we propose an end-to-end sentiment-aware conversational agent based on two models: a reply sentiment prediction model, which leverages the context of the dialogue to predict an appropriate sentiment for the agent to express in its reply; and a text generation model, which is conditioned on the predicted sentiment and the context of the dialogue, to produce a reply that is both context and sentiment appropriate.
Onception: Active Learning with Expert Advice for Real World Machine Translation
Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice.
MT-Telescope: An interactive platform for contrastive evaluation of MT systems
We present MT-Telescope, a visualization platform designed to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems.
Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort
In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging.
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations
Matching variations with their original questions was not trivial with a set of unsupervised baselines, especially for manually created variations.
Graph-Community Detection for Cross-Document Topic Segment Relationship Identification
In this context, we study how different weighting mechanisms influence the discovery of word communities that relate to the different topics found in the documents.
Controlling Complexity in Part-of-Speech Induction
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.
