Giving feedback to students is not just about marking their answers as correct or incorrect, but also finding mistakes in their thought process that led them to that incorrect answer. In this paper, we introduce a machine learning technique for mista
ke captioning, a task that attempts to identify mistakes and provide feedback meant to help learners correct these mistakes. We do this by training a sequence-to-sequence network to generate this feedback based on domain experts. To evaluate this system, we explore how it can be used on a Linguistics assignment studying Grimm's Law. We show that our approach generates feedback that outperforms a baseline on a set of automated NLP metrics. In addition, we perform a series of case studies in which we examine successful and unsuccessful system outputs.
In this paper, we propose a generation challenge called Feedback comment generation for language learners. It is a task where given a text and a span, a system generates, for the span, an explanatory note that helps the writer (language learner) impr
ove their writing skills. The motivations for this challenge are: (i) practically, it will be beneficial for both language learners and teachers if a computer-assisted language learning system can provide feedback comments just as human teachers do; (ii) theoretically, feedback comment generation for language learners has a mixed aspect of other generation tasks together with its unique features and it will be interesting to explore what kind of generation technique is effective against what kind of writing rule. To this end, we have created a dataset and developed baseline systems to estimate baseline performance. With these preparations, we propose a generation challenge of feedback comment generation.
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising app
roach. However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise. We present a concise overview of these challenges and discuss possible solutions.
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus. A common shortcoming of existing approaches is the prerequisi
te of joint annotations across all the stylistic dimensions under consideration. Availability of such dataset across a combination of styles limits the extension of these setups to multiple style dimensions. While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other. In our work, we relax this requirement of jointly annotated data across multiple styles by using independently acquired data across different style dimensions without any additional annotations. We initialize an encoder-decoder setup with transformer-based language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators. Through quantitative and qualitative evaluation, we show the ability of our model to control styles across multiple style dimensions while preserving content of the input text. We compare it against baselines involving cascaded state-of-the-art uni-dimensional style transfer models.
Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by sho
wing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.
Can implicit feedback substitute for explicit ratings in recommender systems?
If so, we could avoid the difficulties associated with gathering explicit ratings from users.
How, then, can we capture useful information unobtrusively, and how might we use that information to make recommendations?
This work presents the details of the construction of the temperature
control system in the center. We used the Atmega8 microcontroller
which has high performance speed and suitable memory.
The employment of multiple antennas at the transmission and reception side for the
formation of a MIMO system contributed significantly to improve the reliability of
transmission and increase the data rate. In the last decade, this system represent
ed the
backbone of wireless communications which paved the way for the development of many
techniques in this area. Therefore, there is a need to study the most important of these
techniques and compare its performance analysis.
This research deals with several closed-loop MIMO techniques : (P-OSM) which
maximizes the minimum Euclidean distance in the received signal constellation in order to
reduce the bit error rate, (X and Y Precoders) which improve the diversity gain of MIMO
system. The aim is to study and analysis the performance of previous techniques in
practical scenarios of wireless communication systems in the presence of limited feedback
channel. The results shows the possibility of practical employment of the P-OSM
technique in a simple way compared to other techniques due to its good performance and
low complexity order.
In this research a proportional integral differential classic (PID
controller) and state feedback controller was designed to control the in
the inverted pendulum and a comparison between all the cases and
choose the most suitable controller using MATLAB / SIMULINK
program
Recent years have witnessed a significant growth in wireless communication as a
result of user demand on high rates of data transmission. Therefore, there is a great
motivation for the application of MIMO systems in many modern communication
stand
ards in order to provide the required data transmission rates. Unfortunately, these
systems are sensitive to poor transmission conditions such as fading. Precoding can
improve the performance of MIMO systems to adapt with channel conditions by knowing
the full channel state information (CSI) at the transmitter. However, a full CSI is often
unrealistic in practice because of the huge amount of this information to be sent back.
Therefore, this information must be reduced and sent through a limited feedback channel.
X and Y Precoder are one of the precoding techniques that have been studied
assuming a full CSI at the transmitter. In this research, we will add a limited feedback
channel to this technique in order to become applicable in practice. The result has shown
that the loss of performance by adding a limited feedback channel may be acceptable.
