  Addressing the issues of who saying what to whom in multi-party conversations
(MPCs) has recently attracted a lot of research attention. However, existing
methods on MPC understanding typically embed interlocutors and utterances into
sequential information flows, or utilize only the superficial of inherent graph
structures in MPCs. To this end, we present a plug-and-play and lightweight
method named graph-induced fine-tuning (GIFT) which can adapt various
Transformer-based pre-trained language models (PLMs) for universal MPC
understanding. In detail, the full and equivalent connections among utterances
in regular Transformer ignore the sparse but distinctive dependency of an
utterance on another in MPCs. To distinguish different relationships between
utterances, four types of edges are designed to integrate graph-induced signals
into attention mechanisms to refine PLMs originally designed for processing
sequential texts. We evaluate GIFT by implementing it into three PLMs, and test
the performance on three downstream tasks including addressee recognition,
speaker identification and response selection. Experimental results show that
GIFT can significantly improve the performance of three PLMs on three
downstream tasks and two benchmarks with only 4 additional parameters per
encoding layer, achieving new state-of-the-art performance on MPC
understanding.
