5 research outputs found
Findings of the 2014 Workshop on Statistical Machine Translation
This paper presents the results of the
WMT14 shared tasks, which included a
standard news translation task, a separate
medical translation task, a task for
run-time estimation of machine translation
quality, and a metrics task. This year, 143
machine translation systems from 23 institutions
were submitted to the ten translation
directions in the standard translation
task. An additional 6 anonymized systems
were included, and were then evaluated
both automatically and manually. The
quality estimation task had four subtasks,
with a total of 10 teams, submitting 57 entries
Findings of the 2015 Workshop on Statistical Machine Translation
This paper presents the results of the
WMT15 shared tasks, which included a
standard news translation task, a metrics
task, a tuning task, a task for run-time
estimation of machine translation quality,
and an automatic post-editing task. This
year, 68 machine translation systems from
24 institutions were submitted to the ten
translation directions in the standard translation
task. An additional 7 anonymized
systems were included, and were then
evaluated both automatically and manually.
The quality estimation task had three
subtasks, with a total of 10 teams, submitting
34 entries. The pilot automatic postediting
task had a total of 4 teams, submitting
7 entries
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task
This paper describes the DCU-Lingo24 submission to WMT 2014 for the Hindi-English translation task. We exploit miscellaneous methods in our system
Promoting user engagement and learning in search tasks by effective document representation
Much research in information retrieval (IR) focuses on optimisation of the rank of relevant retrieval results for single shot ad hoc IR tasks. Relatively little research has been carried out on supporting and promoting user engagement within search tasks. We seek to improve user experience by use of enhanced document snippets to be presented during the search process to promote user engagement with retrieved information. The primary role of document snippets within search has traditionally been to indicate the potential relevance of retrieved items to the user’s information need. Beyond the relevance of an item, it is generally not possible to infer the contents of individual ranked results just by reading the current snippets. We hypothesise that the creation of richer document snippets and summaries, and effective presentation of this information to users will promote effective search and greater user engagement, and support emerging areas such as learning through search.
We generate document summaries for a given query by extracting top relevant sentences from retrieved documents. Creation of these summaries goes beyond exist- ing snippet creation methods by comparing content between documents to take into account novelty when selecting content for inclusion in individual document sum- maries. Further, we investigate the readability of the generated summaries with the overall goal of generating snippets which not only help a user to identify document relevance, but are also designed to increase the user’s understanding and knowledge of a topic gained while inspecting the snippets.
We perform a task-based user study to record the user’s interactions, search be- haviour and feedback to evaluate the effectiveness of our snippets using qualitative and quantitative measures. In our user study, we found that richer snippets generated in this work improved the user experience and topical knowledge, and helped users to learn about the topic effectively