Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data

• URL: http://arxiv.org/abs/2103.04353v1
• Date: Sun, 7 Mar 2021 13:23:51 GMT
• Title: Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
• Abstract summary: We propose a transformer-based encoder-decoder with AraBERT parameters. By initializing the weights of the encoder and decoder with AraBERT pre-trained weights, our model was able to leverage knowledge transfer. Our model achieved a low perplexity value of 17.0 and an increase in 5 BLEU points compared to the previous state-of-the-art model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building human-like conversational models. While Arabic Natural Language Processing has seen significant advances in Natural Language Understanding (NLU) with language models such as AraBERT, Natural Language Generation (NLG) remains a challenge. The shortcomings of NLG encoder-decoder models are primarily due to the lack of Arabic datasets suitable to train NLG models such as conversational agents. To overcome this issue, we propose a transformer-based encoder-decoder initialized with AraBERT parameters. By initializing the weights of the encoder and decoder with AraBERT pre-trained weights, our model was able to leverage knowledge transfer and boost performance in response generation. To enable empathy in our conversational model, we train it using the ArabicEmpatheticDialogues dataset and achieve high performance in empathetic response generation. Specifically, our model achieved a low perplexity value of 17.0 and an increase in 5 BLEU points compared to the previous state-of-the-art model. Also, our proposed model was rated highly by 85 human evaluators, validating its high capability in exhibiting empathy while generating relevant and fluent responses in open-domain settings.

Related papers

• AraModernBERT: Transtokenized Initialization and Long-Context Encoder Modeling for Arabic [0.899920055436881]
  We present AraModernBERT, an adaptation of the ModernBERT encoder architecture to Arabic.<n>We show that transtokenization is essential for Arabic language modeling, yielding dramatic improvements in masked language modeling performance.
  arXiv  Detail & Related papers  (2026-02-10T14:33:28Z)
• On the importance of Data Scale in Pretraining Arabic Language Models [46.431706010614334]
  We conduct a comprehensive study on the role of data in Arabic Pretrained Language Models (PLMs) We reassess the performance of a suite of state-of-the-art Arabic PLMs by retraining them on massive-scale, high-quality Arabic corpora. Our analysis strongly suggests that pretraining data by far is the primary contributor to performance, surpassing other factors.
  arXiv  Detail & Related papers  (2024-01-15T15:11:15Z)
• Enhancing Chat Language Models by Scaling High-quality Instructional Conversations [91.98516412612739]
  We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat. Our objective is to capture the breadth of interactions that a human might have with an AI assistant. We fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA.
  arXiv  Detail & Related papers  (2023-05-23T16:49:14Z)
• Extrapolating Multilingual Understanding Models as Multilingual Generators [82.1355802012414]
  This paper explores methods to empower multilingual understanding models the generation abilities to get a unified model. We propose a textbfSemantic-textbfGuided textbfAlignment-then-Denoising (SGA) approach to adapt an encoder to a multilingual generator with a small number of new parameters.
  arXiv  Detail & Related papers  (2023-05-22T15:33:21Z)
• FBERT: A Neural Transformer for Identifying Offensive Content [67.12838911384024]
  fBERT is a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over $1.4$ million offensive instances. We evaluate fBERT's performance on identifying offensive content on multiple English datasets and we test several thresholds for selecting instances from SOLID. The fBERT model will be made freely available to the community.
  arXiv  Detail & Related papers  (2021-09-10T19:19:26Z)
• Neural Models for Offensive Language Detection [0.0]
  Offensive language detection is an ever-growing natural language processing (NLP) application. We believe contributing to improving and comparing different machine learning models to fight such harmful contents is an important and challenging goal for this thesis.
  arXiv  Detail & Related papers  (2021-05-30T13:02:45Z)
• Generative Adversarial Imitation Learning for Empathy-based AI [0.0]
  Generative adversarial imitation learning (GAIL) is a model-free algorithm that has been shown to provide strong results in imitating complex behaviors in high-dimensional environments. In this paper, we utilize the GAIL model for text generation to develop empathy-based context-aware conversational AI.
  arXiv  Detail & Related papers  (2021-05-27T17:37:37Z)
• AraGPT2: Pre-Trained Transformer for Arabic Language Generation [0.0]
  We develop the first advanced Arabic language generation model, AraGPT2, trained from scratch on a large Arabic corpus of internet text and news articles. Our largest model, AraGPT2-mega, has 1.46 billion parameters, which makes it the largest Arabic language model available. For text generation, our best model achieves a perplexity of 29.8 on held-out Wikipedia articles.
  arXiv  Detail & Related papers  (2020-12-31T09:48:05Z)
• 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)
• Plug-and-Play Conversational Models [62.77150879036442]
  We introduce an approach that does not require further computation at decoding time, while also does not require any fine-tuning of a large language model. We demonstrate, through extensive automatic and human evaluation, a high degree of control over the generated conversational responses with regard to multiple desired attributes.
  arXiv  Detail & Related papers  (2020-10-09T03:17:51Z)
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective [84.78604733927887]
  Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
  arXiv  Detail & Related papers  (2020-10-05T20:49:26Z)
• ParsBERT: Transformer-based Model for Persian Language Understanding [0.7646713951724012]
  This paper proposes a monolingual BERT for the Persian language (ParsBERT) It shows its state-of-the-art performance compared to other architectures and multilingual models. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones.
  arXiv  Detail & Related papers  (2020-05-26T05:05:32Z)

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.