End-to-end Multilingual Coreference Resolution with Mention Head
Prediction
- URL: http://arxiv.org/abs/2209.12516v1
- Date: Mon, 26 Sep 2022 08:45:09 GMT
- Title: End-to-end Multilingual Coreference Resolution with Mention Head
Prediction
- Abstract summary: This paper describes our approach to the CRAC 2022 Shared Task on Multilingual Coreference Resolution.
Our model is based on a state-of-the-art end-to-end coreference resolution system.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper describes our approach to the CRAC 2022 Shared Task on
Multilingual Coreference Resolution. Our model is based on a state-of-the-art
end-to-end coreference resolution system. Apart from joined multilingual
training, we improved our results with mention head prediction. We also tried
to integrate dependency information into our model. Our system ended up in
$3^{rd}$ place. Moreover, we reached the best performance on two datasets out
of 13.
Related papers
- Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters [53.59868121093848]
We introduce Seed-X, a family of open-source language models (LLMs) with 7B parameter size.<n>The base model is pre-trained on a diverse, high-quality dataset encompassing both monolingual and bilingual content across 28 languages.<n>The instruct model is then finetuned to translate by Chain-of-Thought (CoT) reasoning and further enhanced through reinforcement learning (RL) to achieve better generalization across diverse language pairs.
arXiv Detail & Related papers (2025-07-18T03:19:43Z) - HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages [61.28284565925745]
We introduce HelpSteer3-Preference, a high-quality, human-annotated preference dataset comprising of over 40,000 samples.<n>Using HelpSteer3-Preference, we train Reward Models (RMs) that achieve top performance on RM-Bench (82.4%) and JudgeBench (73.7%)<n>This represents a substantial improvement (10% absolute) over the previously best-reported results from existing RMs.
arXiv Detail & Related papers (2025-05-16T17:31:19Z) - Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD [0.0]
This paper presents our end-to-end neural coreference resolution system.
We first establish strong baseline models, including monolingual and cross-lingual variations.
We propose several extensions to enhance performance across diverse linguistic contexts.
arXiv Detail & Related papers (2024-08-29T20:27:05Z) - Light Coreference Resolution for Russian with Hierarchical Discourse
Features [0.0]
We propose a new approach that incorporates rhetorical information into neural coreference resolution models.
We implement an end-to-end span-based coreference resolver using a partially fine-tuned multilingual entity-aware language model LUKE.
Our best model employing rhetorical distance between mentions has ranked 1st on the development set (74.6% F1) and 2nd on the test set (73.3% F1) of the Shared Task.
arXiv Detail & Related papers (2023-06-02T11:41:24Z) - Enhancing Model Performance in Multilingual Information Retrieval with
Comprehensive Data Engineering Techniques [10.57012904999091]
We fine-tune pre-trained multilingual transformer-based models with MIRACL dataset.
Our model improvement is mainly achieved through diverse data engineering techniques.
We secure 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard.
arXiv Detail & Related papers (2023-02-14T12:37:32Z) - Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data.
We design a simple but effective ensemble-based framework that combines various transfer learning techniques.
We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
arXiv Detail & Related papers (2023-01-22T18:22:55Z) - ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution [1.7871207544302354]
We describe the winning submission to the CRAC 2022 Shared Task on Multilingual Coreference Resolution.
Our system first solves mention detection and then coreference linking on the retrieved spans with an antecedent-maximization approach.
arXiv Detail & Related papers (2022-09-15T13:11:39Z) - Distributionally Robust Multilingual Machine Translation [94.51866646879337]
We propose a new learning objective for Multilingual neural machine translation (MNMT) based on distributionally robust optimization.
We show how to practically optimize this objective for large translation corpora using an iterated best response scheme.
Our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.
arXiv Detail & Related papers (2021-09-09T03:48:35Z) - Multilingual Answer Sentence Reranking via Automatically Translated Data [97.98885151955467]
We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems.
The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources.
arXiv Detail & Related papers (2021-02-20T03:52:08Z) - NLP-CIC @ DIACR-Ita: POS and Neighbor Based Distributional Models for
Lexical Semantic Change in Diachronic Italian Corpora [62.997667081978825]
We present our systems and findings on unsupervised lexical semantic change for the Italian language.
The task is to determine whether a target word has evolved its meaning with time, only relying on raw-text from two time-specific datasets.
We propose two models representing the target words across the periods to predict the changing words using threshold and voting schemes.
arXiv Detail & Related papers (2020-11-07T11:27:18Z) - Mixed-Lingual Pre-training for Cross-lingual Summarization [54.4823498438831]
Cross-lingual Summarization aims at producing a summary in the target language for an article in the source language.
We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks like translation and monolingual tasks like masked language models.
Our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.
arXiv Detail & Related papers (2020-10-18T00:21:53Z) - Learning to Learn Morphological Inflection for Resource-Poor Languages [105.11499402984482]
We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem.
Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters.
Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines.
arXiv Detail & Related papers (2020-04-28T05:13:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.