Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTeaching Through Tagging --- Interactive Lexical Semantics
التدريس من خلال وضع العلامات --- دلالات المعجمية التفاعلية
368
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة نناقش جهدا مستمرا لإثراء تعلم الطلاب من خلال إشراكهم بمعنى معنى.الهدف الرئيسي هو قيادة الطلاب لاكتشاف كيف يمكننا تمثيل معنى وحيث تقع حدود نظرياتنا الحالية.الهدف الفرعي هو خلق معنى الموسومة والمعجم المرتبط المرتبط (في حالتنا الوصية).نقدم نتائج وضع العلامات على العديد من النصوص وتشير إلى بعض الطرق التي يمكن بها تحسين عملية وضع العلامات.يقدم مؤلفان من هذه الورقة تجربتهم الخاصة كطلاب.بشكل عام، أبلغ الطلاب أنهم وجدوا وضع العلامات تجربة تخصيبها.تتوفر Corpora والتغييرات المشروحين في Wordnet من خلال Corpus متعدد اللغات NTU و WordNets المرتبطة (NTU-MC).
In this paper we discuss an ongoing effort to enrich students' learning by involving them in sense tagging. The main goal is to lead students to discover how we can represent meaning and where the limits of our current theories lie. A subsidiary goal is to create sense tagged corpora and an accompanying linked lexicon (in our case wordnets). We present the results of tagging several texts and suggest some ways in which the tagging process could be improved. Two authors of this paper present their own experience as students. Overall, students reported that they found the tagging an enriching experience. The annotated corpora and changes to the wordnet are made available through the NTU multilingual corpus and associated wordnets (NTU-MC).
References used
https://aclanthology.org/
rate research
Read More
Shared tasks have a long history and have become the mainstream of NLP research. Most of the shared tasks require participants to submit only system outputs and descriptions. It is uncommon for the shared task to request submission of the system itse
lf because of the license issues and implementation differences. Therefore, many systems are abandoned without being used in real applications or contributing to better systems. In this research, we propose a scheme to utilize all those systems which participated in the shared tasks. We use all participated system outputs as task teachers in this scheme and develop a new model as a student aiming to learn the characteristics of each system. We call this scheme Co-Teaching.'' This scheme creates a unified system that performs better than the task's single best system. It only requires the system outputs, and slightly extra effort is needed for the participants and organizers. We apply this scheme to the SHINRA2019-JP'' shared task, which has nine participants with various output accuracies, confirming that the unified system outperforms the best system. Moreover, the code used in our experiments has been released.
We present a set of assignments for a graduate-level NLP course. Assignments are designed to be interactive, easily gradable, and to give students hands-on experience with several key types of structure (sequences, tags, parse trees, and logical form
s), modern neural architectures (LSTMs and Transformers), inference algorithms (dynamic programs and approximate search) and training methods (full and weak supervision). We designed assignments to build incrementally both within each assignment and across assignments, with the goal of enabling students to undertake graduate-level research in NLP by the end of the course.
We describe work in progress for training a humanoid robot to produce iconic arm and head gestures as part of task-oriented dialogue interaction. This involves the development and use of a multimodal dialog manager for non-experts to quickly program'
the robot through speech and vision. Using this dialog manager, videos of gesture demonstrations are collected. Motor positions are extracted from these videos to specify motor trajectories where collections of motor trajectories are used to produce robot gestures following a Gaussian mixtures approach. Concluding discussion considers how learned representations may be used for gesture recognition by the robot, and how the framework may mature into a system to address language grounding and semantic representation.
Production NMT systems typically need to serve niche domains that are not covered by adequately large and readily available parallel corpora. As a result, practitioners often fine-tune general purpose models to each of the domains their organisation
caters to. The number of domains however can often become large, which in combination with the number of languages that need serving can lead to an unscalable fleet of models to be developed and maintained. We propose Multi Dimensional Tagging, a method for fine-tuning a single NMT model on several domains simultaneously, thus drastically reducing development and maintenance costs. We run experiments where a single MDT model compares favourably to a set of SOTA specialist models, even when evaluated on the domain those baselines have been fine-tuned on. Besides BLEU, we report human evaluation results. MDT models are now live at Booking.com, powering an MT engine that serves millions of translations a day in over 40 different languages.
Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a large amount of monolingual and parallel corpora. Although it is genera
lly acknowledged that parallel corpora are critical for improving the model performance, existing methods are often constrained by the size of parallel corpora, especially for low-resource languages. In this paper, we propose Ernie-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. Our key insight is to integrate back-translation into the pre-training process. We generate pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that Ernie-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks. The codes and pre-trained models will be made publicly available.
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
