Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBuilding a Swedish Open-Domain Conversational Language Model
بناء نموذج لغة محادثة مفتوحة سويدية
384
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نقدم العمل الجاري لتقييم، لمعرفتنا، أول نموذج لغز إذن كبير تم تدريبه على التحدث باللغة السويدية، باستخدام البيانات من Flashback من مناقشة النقاش عبر الإنترنت.نقوم بإجراء دراسة تجريبية للتقييم البشري تشير إلى أن النموذج غالبا ما يكون في الغالب من الاستجابة للمحادثات بطريقة تشبه الإنسان والمعلومات، على مجموعة متنوعة من الموضوعات.في حين أن البيانات من المنتديات عبر الإنترنت يمكن أن تكون مفيدة لبناء أنظمة محادثة، فإننا نفكر في العواقب السلبية التي قد يكون لها تطبيق غير حكيم، والحاجة إلى اتخاذ تدابير فعالة لحماية ضدهم.
We present on-going work of evaluating the, to our knowledge, first large generative language model trained to converse in Swedish, using data from the online discussion forum Flashback. We conduct a human evaluation pilot study that indicates the model is often able to respond to conversations in both a human-like and informative manner, on a diverse set of topics. While data from online forums can be useful to build conversational systems, we reflect on the negative consequences that incautious application might have, and the need for taking active measures to safeguard against them.
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.
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to p
rovide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of asking clarifying questions in open-domain dialogues': (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.
We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and
contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.
In this paper we present a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks, every week consists of lectures, practical sessions and quiz assigments. Three weeks out o
f 12 are followed by Kaggle-style coding assigments. Our course intents to serve multiple purposes: (i) familirize students with the core concepts and methods in NLP, such as language modelling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are build upon these concepts; (iii) to introduce architectures for most most demanded real-life applications, (iii) to develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020 and so far have received positive feedback.
In this work, we are proposing a new model for knowledge discovery in database (KDD) named "SCRUM-BI". It based on SCRUM agile methodology to enhance the way of building Business Intelligence and Data Mining applications. This model characterized as
more adaptive to the changing requirements, priorities and rapidly evolving business environments. SCRUM-BI Also improves and enhances the process of knowledge obtaining and sharing, which contributes to support strategic decision-making.
The model was validated using a case study on the telecommunications sector in Syria.
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
