Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis

Meta-Learning a Cross-lingual Manifold for Semantic Parsing Tom Sherborne and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB, UK tom.sherborne@ed.ac.uk, mlap@inf.ed.ac.uk Abstract Localizing a semantic parser to support new languages requires effective cross-lingual gen- eralization. Recent work has found success with machine-translation or zero-shot meth- ods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage min- imal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algo- rithm to train a semantic parser with maximal sample efficiency during cross-lingual trans- fer. Our algorithm uses high-resource lan- guages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling ≤10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling ≤10% of train- ing data.1 1 Introduction A semantic parser maps natural language (NL) utterances to logical forms (LF) or executable pro- grams in some machine-readable language (e.g., SQL). Recent improvement in the capability of semantic parsers has focused on domain transfer within English (Su and Yan, 2017; Suhr et al., 2020), compositional generalization (Yin and Neubig, 2017; Herzig and Berant, 2021; Scholak et al., 2021), and, more recently, cross-lingual methods (Duong et al., 2017; Susanto and Lu, 2017b; Richardson et al., 2018). Within cross-lingual semantic parsing, there has been an effort to bootstrap parsers with min- 1Our code and data are available at github.com /tomsherborne/xgr. imal data to avoid the cost and labor required to support new languages. Recent proposals in- clude using machine translation to approximate training data for supervised learning (Moradshahi et al., 2020; Sherborne et al., 2020; Nicosia et al., 2021) and zero-shot models, which engineer cross- lingual similarity with auxiliary losses (van der Goot et al., 2021; Yang et al., 2021; Sherborne and Lapata, 2022). These shortcuts bypass costly data annotation but present limitations such as ‘‘translationese’’ artifacts from machine transla- tion (Koppel and Ordan, 2011) or undesirable domain shift (Sherborne and Lapata, 2022). How- ever, annotating a minimally sized data sample can potentially overcome these limitations while incurring significantly reduced costs compared to full dataset translation (Garrette and Baldridge, 2013). We argue that a few-shot approach is more realistic for an engineer motivated to support ad- ditional languages for a database—as one can rapidly retrieve a high-quality sample of transla- tions and combine these with existing supported languages (i.e., English). Beyond semantic pars- ing, cross-lingual few-shot approaches have also succeeded at leveraging a small number of anno- tations within a variety of tasks (Zhao et al., 2021, inter alia) including natural language inference, paraphrase identification, part-of-speech-tagging, and named-entity recognition. Recently, the ap- plication of meta-learning to domain generali- zation has further demonstrated capability for models to adapt to new domains with small sam- …

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