Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction

Anonymous

Abstract:

Domain adaptation methods often exploit domain-transferable input features, a.k.a. pivots.
The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from \textit{restaurants} to \textit{laptops}).
In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction.
We present several analyses supporting this conjecture, via experiments with four linguistic dependency formalisms to represent relation patterns.
Following, we present an aspect term extraction method that drives models to consider opinion--aspect relations via explicit multitask objectives.
This method provides significant performance gains, even on top of a prior state-of-the-art linguistically-informed model, which are shown in analysis to stem from the relational pivoting signal.