Multi-Resolution Language Grounding with Weak Supervision

By Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ali Farhadi. 2014

13 citations recorded by the source corpus.

Language is given meaning through its correspondence with a world representation. This correspondence can be at multiple levels of granularity or resolutions. In this paper, we introduce an approach to multi-resolution language grounding in the extremely challenging domain of professional soccer commentaries. We define and optimize a factored objective function that allows us to leverage discourse structure and the compositional nature of both language and game events. We show that finer resolution grounding helps coarser resolution grounding, and vice versa. Our method results in an F1 improvement of more than 48% versus the previous state of the art for fine-resolution grounding 1 .

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Parsing Algebraic Word Problems into Equations (2015, background): “More closely related is work on language grounding, whose goal is the interpretation of a sentence in the context of a world representation (Branavan et al., 2009; Liang et al., 2009; Chen et al., 2010; Bordes et al., 2010; Feng and Lapata, 2010; Hajishirzi et al., 2011; Matuszek et al., 2012; Hajis”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “…the work by Barzilay and Lapata (2005) to multiple domains (soccer and weather), relying on weakly supervised techniques; in a similar vein, Koncel-Kedziorski et al. (2014) presented a weakly supervised multilevel approach, to deal with the fact that there is no one-to-one correspondence…”
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020, methodology): “Each target text is split into meaningful fragments and aligned with corresponding data records, either by hand-engineered rules (Kukich, 1983; McKeown, 1992) or statistical induction (Liang et al., 2009; Koncel-Kedziorski et al., 2014; Qin et al., 2018).”
Solving Geometry Problems: Combining Text and Diagram Interpretation (2015, background): “…language acquisition research (Branavan et al., 2012; Vogel and Jurafsky, 2010; Chen et al., 2010; Hajishirzi et al., 2011; Liang et al., 2009; Koncel-Kedziorski et al., 2014; Bordes et al., 2010; Kim and Mooney, 2013; Angeli and Manning, 2014; Hixon et al., 2015; Koncel-Kedziorski et al.,…”