In this paper, we present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its o
wn challenges: duplication of content and uneven distribution of topics. We adopt a similar setup as Dense Passage Retrieval (DPR) and test various bi-encoders on this dataset. Our experiments reveal that a multilingual model based on XLM-RoBERTa achieves the best results, except for English. Lower resources languages seem to learn from one another as a multilingual model achieves a higher MRR than language-specific ones. Our qualitative analysis reveals the brittleness of the model on simple word changes. We publicly release our dataset, model, and training script.
In this paper we present a deep learning code completion model for the R language. We introduce several techniques to utilize language modeling based architecture in the code completion task. With these techniques, the model requires low resources, b
ut still achieves high quality. We also present an evaluation dataset for the R language completion task. Our dataset contains multiple autocompletion usage contexts that provides robust validation results. The dataset is publicly available.
Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language inst
ructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.
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.
Abstract We study continual learning for natural language instruction generation, by observing human users' instruction execution. We focus on a collaborative scenario, where the system both acts and delegates tasks to human users using natural langu
age. We compare user execution of generated instructions to the original system intent as an indication to the system's success communicating its intent. We show how to use this signal to improve the system's ability to generate instructions via contextual bandit learning. In interaction with real users, our system demonstrates dramatic improvements in its ability to generate language over time.
Due to the prominent place composition holds in teaching French
to non-native speakers, this article examines the effectiveness of
formative evaluation in improving the writing skills of second year
students in the department of French language. I
n addition, by
showing the difference in the scoring results between the students
who followed the formative evaluation and those who didn’t, this
article aims to illustrate the impact of distributing copies of the
grading scale to students during the training period.
غيَرت معالجات Intel Atom المتعددة النواة توقعات المستخدمين لفاعلية واداء المعالجات.مع أستهلاك قوي وكلفة اقل, قائدةً تصميم جديد طوَرت انتل عائلة لفاعلية جيدة كفايةَ محققةَ شروط القوة والتسويق المتذايد والقوي
