Argumentation　mining　has　recently　become　a　hot　topic　in　the　field　of　data　mining　and　natural　language　processing.　Its　main　task　is　automatic　identification　of　argumentative　structures　in　persuasive　essays　so　as　to　help　people　better　understand　the　massive　text　information.　A　persuasive　essay　usually　consists　of　a　series　of　argument　components.　The　types　of　argument　components　are　generally　classified　into　claims　or　premises,　and　the　types　of　relationship　between　argument　components　are　commonly　classified　into　support　or　attack.　Argumentation　mining　typically　contains　three　consecutive　subtasks,　i.e.,　(1)　Argument　component　boundary　detection　(ACBD　Task),　which　involves　separating　argument　component　from　non-argumentative　text　units　and　identifying　the　argument　component　boundaries;　(2)　Argument　component　identification　(ACI　Task),　whose　goal　is　to　classify　argument　components　into　different　types,　such　as　claims　or　premises;　(3)　Argument　component　relation　identification　(RI　Task),　which　aims　to　identify　the　relationship　type　between　argument　components,　such　as　support　or　attack.　Recently,　many　researchers　have　proposed　a　series　of　argumentation　mining　models　and　made　brilliant　improvement.　However,　most　of　the　existing　approaches　mainly　focus　on　modeling　each　subtask　and　ignore　the　correlation　information　among　the　three　subtasks,　resulting　in　low　performance.　In　addition,　some　of　the　approaches　utilize　pipeline　methods　to　jointly　model　three　subtasks.　The　pipeline　methods　still　consider　each　subtask　independently,　and　train　separated　models　for　each　subtask,　which　could　lead　to　error　propagation　and　redundant　information　in　the　training　process.　More　specifically,　the　error　of　argument　component　boundary　recognition　module　affects　the　following　argument　component　classification　performance.　Similarly,　the　error　of　argument　component　classification　also　influences　the　performance　of　argument　component　relation　identification.　To　solve　these　problems　above,　we　propose　a　multi-task　iterative　learning　method　which　assumes　that　tags　predicting　for　one　task　could　be　useful　feature　for　other　tasks,　and　joints　three　subtasks　in　parallel　to　learn　together　for　argumentation　mining.　Firstly,　we　obtain　the　shallow　shared　parameters　of　the　text　character　and　word　level　by　utilizing　the　deep　Convolutional　Neural　Network　(CNN)　and　the　highway　network.　And　then,　the　Bi-directional　LSTM　neural　network　is　trained　to　solve　three　subtasks　at　the　same　time　to　avoid　error　propagation.　In　the　training　process,　the　correlation　information　among　each　subtask　is　used　to　overcome　the　generation　of　redundant　information.　Finally,　the　output　of　three　subtasks　is　concatenated　as　the　input　for　the　next　iteration　to　improve　the　performance.　Multi-Task　Learning(MTL)　is　an　important　machine　learning　mechanism　and　improves　the　generalization　performance　by　learning　a　task　together　with　other　related　tasks.　Our　model　based　on　MTL　could　iterative　utilize　predicting　tags＇　distribution　of　each　task　explicitly.　Experimental　results　on　student　essays　published　by　the　UKP　laboratory　in　Germany　show　that,　compared　to　the　state-of-the-art　models,　our　model　improve　2.74%　on　accuracy,　1.05%　on　＇F1(100%)＇　and　1.19%　on　＇F1(50%)＇,　which　verify　the　validity　of　our　model.　Besides,　results　also　show　that　the　performance　of　multi-task　learning　is　better　than　single　task　learning.　©　2019,　Science　Press.　All　right　reserved.