We take a different approach to representation learning, addressing two potential limitations of <cite>Miwa and Bansal (2016)</cite> . First, <cite>Miwa and Bansal (2016)</cite> rely on external syntactic parsers for obtaining syntactic information, which is crucial for relation extraction (Culotta and Sorensen, 2004; Zhou et al., 2005; Bunescu and Mooney, 2005; Qian et al., 2008) . However, parsing errors can lead to encoding inaccuracies of tree-LSTMs, thereby hurting relation extraction potentially. We take an alternative approach to integrating syntactic information, by taking the hidden LSTM layers of a bi-affine attention parser (Dozat and Manning, 2016) to augment input representations.