Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSmall Model and In-Domain Data Are All You Need
النموذج الصغير والبيانات داخل المجال كلها تحتاجها
122
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
شاركت في WMT مشاركتها الأخبار مهمة الترجمة والتركيز على زوج واحد في لغة الموارد عالية: الإنجليزية والصينية (اتجاهين، صينيين إلى اللغة الإنجليزية والإنجليزية إلى الصينية).تركز الأنظمة المقدمة (Zenghuimt) على تنظيف البيانات، واختيار البيانات، والترجمة مرة أخرى ونموذج النموذج.تتضمن التقنيات التي استخدمتها لتصفية البيانات والاختيار التصفية حسب القواعد ونموذج اللغة ومحاذاة Word.لقد استخدمت نموذجا أساسا للترجمة المدربين على Corpus الأولي للحصول على الإصدارات المستهدفة من مجموعات اختبار WMT21، ثم استخدمت نماذج اللغة لمعرفة البيانات أحادية الأبدية التي تشبه الإصدار المستهدف من مجموعة الاختبار، ثم تم استخدام هذه البيانات الأحاديةللقيام الترجمة مرة أخرى.في مجموعة الاختبار، تحقيق أفضل أنظمة بلدي المقدمة 35.9 و 32.2 بلو للإنجليزية إلى اتجاهات اللغة الإنجليزية والصينية إلى الإنجليزية على التوالي، وهي مرتفعة للغاية بالنسبة لطراز صغير.
I participated in the WMT shared news translation task and focus on one high resource language pair: English and Chinese (two directions, Chinese to English and English to Chinese). The submitted systems (ZengHuiMT) focus on data cleaning, data selection, back translation and model ensemble. The techniques I used for data filtering and selection include filtering by rules, language model and word alignment. I used a base translation model trained on initial corpus to obtain the target versions of the WMT21 test sets, then I used language models to find out the monolingual data that is most similar to the target version of test set, such monolingual data was then used to do back translation. On the test set, my best submitted systems achieve 35.9 and 32.2 BLEU for English to Chinese and Chinese to English directions respectively, which are quite high for a small model.
References used
https://aclanthology.org/
rate research
Read More
The use of pretrained language models, fine-tuned to perform a specific downstream task, has become widespread in NLP. Using a generic language model in specialized domains may, however, be sub-optimal due to differences in language use and vocabular
y. In this paper, it is investigated whether an existing, generic language model for Swedish can be improved for the clinical domain through continued pretraining with clinical text. The generic and domain-specific language models are fine-tuned and evaluated on three representative clinical NLP tasks: (i) identifying protected health information, (ii) assigning ICD-10 diagnosis codes to discharge summaries, and (iii) sentence-level uncertainty prediction. The results show that continued pretraining on in-domain data leads to improved performance on all three downstream tasks, indicating that there is a potential added value of domain-specific language models for clinical NLP.
Ever since neural models were adopted in data-to-text language generation, they have invariably been reliant on extrinsic components to improve their semantic accuracy, because the models normally do not exhibit the ability to generate text that reli
ably mentions all of the information provided in the input. In this paper, we propose a novel decoding method that extracts interpretable information from encoder-decoder models' cross-attention, and uses it to infer which attributes are mentioned in the generated text, which is subsequently used to rescore beam hypotheses. Using this decoding method with T5 and BART, we show on three datasets its ability to dramatically reduce semantic errors in the generated outputs, while maintaining their state-of-the-art quality.
In this paper, we describe our system entry for Shared Task 8 at SMM4H-2021, which is on automatic classification of self-reported breast cancer posts on Twitter. In our system, we use a transformer-based language model fine-tuning approach to automa
tically identify tweets in the self-reports category. Furthermore, we involve a Gradient-based Adversarial fine-tuning to improve the overall model's robustness. Our system achieved an F1-score of 0.8625 on the Development set and 0.8501 on the Test set in Shared Task-8 of SMM4H-2021.
Style transfer aims to rewrite a source text in a different target style while preserving its content. We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source--target) data outpe
rforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. In practice, we adopt a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model (BART). First, we strengthen the model's ability to rewrite by further pre-training BART on both an existing collection of generic paraphrases, as well as on synthetic pairs created using a general-purpose lexical resource. Second, through an iterative back-translation approach, we train two models, each in a transfer direction, so that they can provide each other with synthetically generated pairs, dynamically in the training process. Lastly, we let our best resulting model generate static synthetic pairs to be used in a supervised training regime. Besides methodology and state-of-the-art results, a core contribution of this work is a reflection on the nature of the two tasks we address, and how their differences are highlighted by their response to our approach.
This paper discusses different approaches to the Toxic Spans Detection task. The problem posed by the task was to determine which words contribute mostly to recognising a document as toxic. As opposed to binary classification of entire texts, word-le
vel assessment could be of great use during comment moderation, also allowing for a more in-depth comprehension of the model's predictions. As the main goal was to ensure transparency and understanding, this paper focuses on the current state-of-the-art approaches based on the explainable AI concepts and compares them to a supervised learning solution with word-level labels. The work consists of two xAI approaches that automatically provide the explanation for models trained for binary classification of toxic documents: an LSTM model with attention as a model-specific approach and the Shapley values for interpreting BERT predictions as a model-agnostic method. The competing approach considers this problem as supervised token classification, where models like BERT and its modifications were tested. The paper aims to explore, compare and assess the quality of predictions for different methods on the task. The advantages of each approach and further research direction are also discussed.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
