SemEval-2022 Task 8: Multi-lingual News Article Similarity

• URL: http://arxiv.org/abs/2208.09715v1
• Date: Sat, 20 Aug 2022 16:06:53 GMT
• Title: SemEval-2022 Task 8: Multi-lingual News Article Similarity
• Authors: Nikhil Goel and Ranjith Reddy
• Abstract summary: This work is about finding the similarity between a pair of news articles. There are seven different objective similarity metrics provided in the dataset for each pair and the news articles are in multiple different languages. On top of the pre-trained embedding model, we calculated cosine similarity for baseline results and feed-forward neural network was then trained on top of it to improve the results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work is about finding the similarity between a pair of news articles. There are seven different objective similarity metrics provided in the dataset for each pair and the news articles are in multiple different languages. On top of the pre-trained embedding model, we calculated cosine similarity for baseline results and feed-forward neural network was then trained on top of it to improve the results. We also built separate pipelines for each similarity metric for feature extraction. We could see significant improvement from baseline results using feature extraction and feed-forward neural network.

Related papers

• Semantic Alignment of Multilingual Knowledge Graphs via Contextualized Vector Projections [8.709638469928448]
  Cross-lingual alignment system using embedding based cosine similarity matching.<n>We use a fine-tuned transformer based multilingual model for generating better embeddings.<n>We have evaluated our work on OAEI-2022 multifarm track.
  arXiv  Detail & Related papers  (2025-12-22T11:02:30Z)
• Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory [64.06519549649495]
  We provide the definition of what we call functionally equivalent features. These features produce equivalent output under certain transformations. We propose an efficient algorithm named Iterative Feature Merging.
  arXiv  Detail & Related papers  (2023-10-10T16:27:12Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning [93.38239238988719]
  We propose to enable deep neural networks with the ability to learn the sample relationships from each mini-batch. BatchFormer is applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications.
  arXiv  Detail & Related papers  (2022-03-03T05:31:33Z)
• Deep ensembles in bioimage segmentation [74.01883650587321]
  In this work, we propose an ensemble of convolutional neural networks (CNNs) In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers. The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment.
  arXiv  Detail & Related papers  (2021-12-24T05:54:21Z)
• Similarity and Matching of Neural Network Representations [0.0]
  We employ a toolset -- dubbed Dr. Frankenstein -- to analyse the similarity of representations in deep neural networks. We aim to match the activations on given layers of two trained neural networks by joining them with a stitching layer.
  arXiv  Detail & Related papers  (2021-10-27T17:59:46Z)
• Hierarchical Similarity Learning for Language-based Product Image Retrieval [40.83290730640458]
  This paper focuses on the cross-modal similarity measurement, and proposes a novel Hierarchical Similarity Learning network. Experiments on a large-scale product retrieval dataset demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2021-02-18T14:23:16Z)
• Input Similarity from the Neural Network Perspective [7.799648230758492]
  A neural network trained on a dataset with noisy labels reaches almost perfect accuracy. We show how to use a similarity measure to estimate sample density. We also propose to enforce that examples known to be similar should also be seen as similar by the network.
  arXiv  Detail & Related papers  (2021-02-10T04:57:30Z)
• Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network [83.64416937454801]
  Job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms. We propose a novel multi-view co-teaching network from sparse interaction data for job-resume matching. Our model is able to outperform state-of-the-art methods for job-resume matching.
  arXiv  Detail & Related papers  (2020-09-25T03:09:54Z)
• Logic Constrained Pointer Networks for Interpretable Textual Similarity [11.142649867439406]
  We introduce a novel pointer network based model with a sentinel gating function to align constituent chunks. We improve this base model with a loss function to equally penalize misalignments in both sentences, ensuring the alignments are bidirectional. The model achieves an F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk alignment task.
  arXiv  Detail & Related papers  (2020-07-15T13:01:44Z)
• Combining word embeddings and convolutional neural networks to detect duplicated questions [0.0]
  We propose a simple approach to identifying semantically similar questions by combining the strengths of word embeddings and Convolutional Neural Networks. Our network is trained on the Quora dataset, which contains over 400k question pairs.
  arXiv  Detail & Related papers  (2020-06-08T12:30:25Z)
• Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages [112.65994041398481]
  We propose a Bayesian generative model for the space of neural parameters. We infer the posteriors over such latent variables based on data from seen task-language combinations. Our model yields comparable or better results than state-of-the-art, zero-shot cross-lingual transfer methods.
  arXiv  Detail & Related papers  (2020-01-30T16:58:56Z)

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.