Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

• URL: http://arxiv.org/abs/2203.16474v1
• Date: Wed, 30 Mar 2022 17:11:48 GMT
• Title: Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models
• Authors: Harshvardhan Srivastava
• Abstract summary: We describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation. We train an end to end model to extract meaningful information from different languages and test our model on two seperate datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different brain triggers , however there seems to be some uniform indicators. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features. We train an end to end model to extract meaningful information from different languages and test our model on two seperate datasets. We compare different transformer models and show ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 for the shared task.

Related papers

• Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction [23.45902601618188]
  Language models have demonstrated impressive ability in context understanding and generative performance. We propose LMTraj (Language-based Multimodal Trajectory predictor), which recasts the trajectory prediction task into a sort of question-answering problem. We show that the language-based model can be a powerful pedestrian trajectory predictor, and outperforms existing numerical-based predictor methods.
  arXiv  Detail & Related papers  (2024-03-27T11:06:44Z)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models [12.759281077118567]
  Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages. We build upon some of the existing techniques for predicting the zero-shot performance on a task, by modeling it as a multi-task learning problem.
  arXiv  Detail & Related papers  (2022-05-12T14:47:03Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• Team \'UFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models [9.087729124428467]
  We describe our systems for the CMCL 2022 shared task on predicting eye-tracking information. Our submissions achieved an average MAE of 5.72 and ranked 5th in the shared task.
  arXiv  Detail & Related papers  (2022-04-11T10:43:34Z)
• Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation [49.916963624249355]
  A UNMT model is trained on the pseudo parallel data with translated source, and natural source sentences in inference. The source discrepancy between training and inference hinders the translation performance of UNMT models. We propose an online self-training approach, which simultaneously uses the pseudo parallel data natural source, translated target to mimic the inference scenario.
  arXiv  Detail & Related papers  (2022-03-16T04:50:27Z)
• A Variational Hierarchical Model for Neural Cross-Lingual Summarization [85.44969140204026]
  Cross-lingual summarization () is to convert a document in one language to a summary in another one. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model. We propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder.
  arXiv  Detail & Related papers  (2022-03-08T02:46:11Z)
• Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction [0.0]
  Natural Language Pro-cessing uses human-derived behavioral data like eye-tracking data to augment the neural nets to solve arange of tasks spanning syntax and semantics. In this paper,we use the ZuCo 1.0 and ZuCo 2.0 dataset to explore differ-ent linguistic models to directly predict thesegaze features for each word with respect to itssentence.
  arXiv  Detail & Related papers  (2021-10-09T06:46:48Z)
• TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction [25.99947358445936]
  We describe our submission to the CMCL 2021 shared task on predicting human reading patterns. Our model uses RoBERTa with a regression layer to predict 5 eye-tracking features. Our final submission achieves a MAE score of 3.929, ranking 3rd place out of 13 teams that participated in this task.
  arXiv  Detail & Related papers  (2021-04-15T05:29:13Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)

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.