The Machine Translation Leaderboard
نویسندگان
چکیده
Much of an instructor’s time is spent on the management and grading of homework. We present the Machine Translation Leaderboard, a platform for managing, displaying, and automatically grading homework assignments. It runs on Google App Engine, which provides hosting and usermanagement services. Among its many features are the ability to easily define new assignments, manage submission histories, maintain a development / test set distinction, and display a leaderboard. An entirely new class can be set up in minutes with minimal configuration. It comes pre-packaged with five assignments used in a graduate course on machine translation.
منابع مشابه
Toward a Better Understanding of Leaderboard
The leaderboard in machine learning competitions is a tool to show the performance of various participants and to compare them. However, the leaderboard quickly becomes no longer accurate, due to hack or overfitting. This article gives two advices to avoid this. It also points out that the Ladder leaderboard successfully prevents this with Õ( −3) samples in the validation set.
متن کاملThe Ladder: A Reliable Leaderboard for Machine Learning Competitions
The organizer of a machine learning competition faces the problem of maintaining an accurate leaderboard that faithfully represents the quality of the best submission of each competing team. What makes this estimation problem particularly challenging is its sequential and adaptive nature. As participants are allowed to repeatedly evaluate their submissions on the leaderboard, they may begin to ...
متن کاملFilter and Match Approach to Pair-wise Web URI Linking
This paper describes the method and results of our approach, submitted as team ‘NLPCruise’ at ALTA shared task 2016. The goal of the shared task is to predict whether two given web Uniform Resource Identifiers (URIs) correspond to the same entity or not. Retrieving the URI content in addition to the dataset provided, we built a two stage filter and match technique that utilises search engine sc...
متن کاملClimbing a shaky ladder: Better adaptive risk estimation
We revisit the leaderboard problem introduced by Blum and Hardt (2015) in an effort to reduce overfitting in machine learning benchmarks. We show that a randomized version of their Ladder algorithm achieves leaderboard error O(1/n0.4) compared with the previous best rate of O(1/n1/3). Short of proving that our algorithm is optimal, we point out a major obstacle toward further progress. Specific...
متن کاملHorizontal and Vertical Ensemble with Deep Representation for Classification
Representation learning, especially which by using deep learning, has been widely applied in classification. However, how to use limited size of labeled data to achieve good classification performance with deep neural network, and how can the learned features further improve classification remain indefinite. In this paper, we propose Horizontal Voting Vertical Voting and Horizontal Stacked Ense...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Prague Bull. Math. Linguistics
دوره 102 شماره
صفحات -
تاریخ انتشار 2014