Opinion　mining　has　raised　growing　interest　both　in　industry　and　academia　in　the　past　decade.　Opinion　role　labeling　(ORL)　is　a　task　to　extract　opinion　holder　and　target　from　natural　language　to　answer　the　question　＂who　express　what＂.　Recent　years,　neural　network　based　methods　with　additional　lexical　and　syntactic　features　have　achieved　state-of-the-art　performances　in　similar　tasks.　Moreover,　Bidirectional　Encoder　Representations　from　Transformers　(BERT)　has　shown　impressive　performances　among　a　variety　of　natural　language　processing　(NLP)　tasks.　To　investigate　BERT　based　end-to-end　model　in　ORL,　we　propose　models　using　BERT,　Bidirectional　Long　short-term　Memory　(BILSTM)　and　Conditional　Random　Field　(CRF)　to　jointly　extract　opinion　roles　(e.g.,　opinion　holder　and　target).　Experimental　results　show　that　our　models　achieve　remarkable　scores　without　using　extra　syntactic　and/or　semantic　features.　To　our　best　knowledge,　we　are　among　the　pioneers　to　successfully　integrate　BERT　in　this　manner.　Our　work　contributes　to　the　improvement　of　state-of-the-art　aspect-level　opinion　mining　methods　and　providing　strong　baselines　for　future　work.