19,777 research outputs found
Revisiting Challenges in Data-to-Text Generation with Fact Grounding
Data-to-text generation models face challenges in ensuring data fidelity by
referring to the correct input source. To inspire studies in this area, Wiseman
et al. (2017) introduced the RotoWire corpus on generating NBA game summaries
from the box- and line-score tables. However, limited attempts have been made
in this direction and the challenges remain. We observe a prominent bottleneck
in the corpus where only about 60% of the summary contents can be grounded to
the boxscore records. Such information deficiency tends to misguide a
conditioned language model to produce unconditioned random facts and thus leads
to factual hallucinations. In this work, we restore the information balance and
revamp this task to focus on fact-grounded data-to-text generation. We
introduce a purified and larger-scale dataset, RotoWire-FG (Fact-Grounding),
with 50% more data from the year 2017-19 and enriched input tables, hoping to
attract more research focuses in this direction. Moreover, we achieve improved
data fidelity over the state-of-the-art models by integrating a new form of
table reconstruction as an auxiliary task to boost the generation quality.Comment: Best Paper Runner-up at INLG 2019 (12th International Conference on
Natural Language Generation
Language Grounded QFormer for Efficient Vision Language Understanding
Large-scale pretraining and instruction tuning have been successful for
training general-purpose language models with broad competencies. However,
extending to general-purpose vision-language models is challenging due to the
distributional diversity in visual inputs. A recent line of work explores
vision-language instruction tuning, taking inspiration from the Query
Transformer (QFormer) approach proposed in BLIP-2 models for bridging frozen
modalities. However, these approaches rely heavily on large-scale multi-modal
pretraining for representation learning before eventual finetuning, incurring a
huge computational overhead, poor scaling, and limited accessibility. To that
end, we propose a more efficient method for QFormer-based vision-language
alignment and demonstrate the effectiveness of our strategy compared to
existing baselines in improving the efficiency of vision-language pretraining.Comment: Preprint Under Revie
Transitioning Applications to Semantic Web Services: An Automated Formal Approach
Semantic Web Services have been recognized as a promising technology that exhibits huge commercial potential, and attract significant attention from both industry and the research community. Despite expectations being high, the industrial take-up of Semantic Web Service technologies has been slower than expected. One of the main reasons is that many systems have been developed without considering the potential of the web in integrating services and sharing resources. Without a systematic methodology and proper tool support, the migration from legacy systems to Semantic Web Service-based systems can be a very tedious and expensive process, which carries a definite risk of failure. There is an urgent need to provide strategies which allow the migration of legacy systems to Semantic Web Services platforms, and also tools to support such a strategy. In this paper we propose a methodology for transitioning these applications to Semantic Web Services by taking the advantage of rigorous mathematical methods. Our methodology allows users to migrate their applications to Semantic Web Services platform automatically or semi-automatically
Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text
Acknowledgments We would like to thank our reviewers for their insightful feedback and questions. The work presented here is partially funded by the Engineering and Physical Sciences Research Council (EPSRC), which funds Craig Thomson under a National Productivity Investment Fund Doctoral Studentship (EP/R512412/1).Peer reviewedPublisher PD
- ā¦