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| "TABREF0": { |
| "content": "<table><tr><td>\u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76</td><td>105</td></tr><tr><td colspan=\"2\">\u6211\u5011\u5728\u63a5\u4e0b\u4f86\u7684\u5c0f\u7bc0\u8a0e\u8ad6\u8207\u6574\u7406\u8a18\u61b6\u7db2\u8def\u76f8\u95dc\u9818\u57df\u7814\u7a76\u6587\u737b\uff1b\u7b2c\u4e09\u7bc0\u70ba\u7814\u7a76\u65b9\u6cd5\u8207</td></tr><tr><td colspan=\"2\">\u8a2d\u8a08\uff0c\u5c0d\u65bc\u672c\u8ad6\u6587\u7814\u7a76\u65b9\u5f0f\u8207\u65b9\u6cd5\u505a\u4e00\u7cfb\u5217\u7684\u6574\u7406\u8207\u8aaa\u660e\uff1b\u7b2c\u56db\u7bc0\u5247\u70ba\u5be6\u9a57\u7d50\u679c\u8207\u5206\u6790\uff0c</td></tr><tr><td colspan=\"2\">\u6bd4\u8f03\u6539\u5584\u524d\u5f8c\u6a21\u578b\u7684\u8868\u73fe\uff0c\u9a57\u8b49\u6240\u63a1\u7528\u65b9\u6cd5\u7684\u53ef\u884c\u6027\u8207\u50f9\u503c\uff1b\u6700\u5f8c\u4e00\u7bc0\u70ba\u7d50\u8ad6\u8207\u5efa\u8b70\uff0c</td></tr><tr><td>\u7e3d\u7d50\u672c\u8ad6\u6587\u6240\u63a1\u7528\u65b9\u6cd5\u7684\u512a\u7f3a\u9ede\uff0c\u4ee5\u53ca\u672a\u4f86\u53ef\u5617\u8a66\u7684\u65b9\u5411\u3002</td><td/></tr><tr><td>2. \u6587\u737b\u56de\u9867 (Literature Review)</td><td/></tr><tr><td colspan=\"2\">\u8a18\u61b6\u7db2\u8def(Memory Networks)\u4e3b\u8981\u904b\u7528\u65bc\u554f\u7b54\u4efb\u52d9\u8207\u60c5\u611f\u5206\u6790\u7b49\u61c9\u7528\u4e0a\uff0c\u63a1\u7528\u5916\u90e8\u8a18\u61b6\u7684</td></tr><tr><td colspan=\"2\">\u65b9\u5f0f\u5132\u5b58\u5148\u9a57\u77e5\u8b58\uff0c\u900f\u904e\u6ce8\u610f\u529b\u6a5f\u5236\u627e\u5230\u8207\u554f\u984c\u76f8\u95dc\u7684\u8a18\u61b6\u5167\u5bb9\uff0c\u518d\u5229\u7528\u63a8\u7406\u6a21\u7d44\u5f9e\u554f</td></tr><tr><td colspan=\"2\">\u984c\u8207\u76f8\u95dc\u8a18\u61b6\u5f97\u51fa\u6700\u7d42\u7b54\u6848\u3002\u8a18\u61b6\u7db2\u8def\u7531\u8a31\u591a\u6a21\u7d44\u7d44\u5408\u800c\u6210\uff0c\u5404\u500b\u90e8\u5206\u53ef\u7531\u8a2d\u8a08\u8005\u63a1\u7528</td></tr><tr><td>\u4e0d\u540c\u65b9\u5f0f\u5be6\u73fe\uff0c\u672c\u5c0f\u7bc0\u4ecb\u7d39\u8a18\u61b6\u7db2\u8def\u76f8\u95dc\u7814\u7a76\uff0c\u4ee5\u53ca\u8a9e\u8a00\u6a21\u578b\u7684\u76f8\u95dc\u7406\u8ad6\u3002</td><td/></tr><tr><td colspan=\"2\">\u95dc\u6ce8\u3002\u5728\u81ea\u7136\u8a9e\u8a00\u9818\u57df\u4e2d\uff0c\u804a\u5929\u6a5f\u5668\u4eba\u3001\u554f\u7b54\u4efb\u52d9\u7b49\uff0c\u90fd\u5177\u6709\u5e8f\u5217\u8cc7\u6599\u7684\u7279\u6027\uff0c\u4e5f\u5c31\u662f</td></tr><tr><td colspan=\"2\">\u6587\u53e5\u5b57\u8a5e\u6709\u6642\u9593\u5148\u5f8c\u7684\u95dc\u4fc2\uff0c\u56e0\u6b64\u8a08\u7b97\u7684\u904e\u7a0b\u9700\u8981\u7d66\u8207\u6a21\u578b\u8a5e\u5e8f\u8cc7\u8a0a\uff0c\u6216\u662f\u4f9d\u7167\u9806\u5e8f\u8f38</td></tr><tr><td colspan=\"2\">\u5165\u81f3\u6a21\u578b\u5167\u3002\u5176\u4e2d\u8a18\u61b6\u6a21\u578b\u4f7f\u7528\u5916\u90e8\u8a18\u61b6\u7684\u65b9\u5f0f\u5132\u5b58\u6587\u672c\u6216\u5148\u9a57\u77e5\u8b58\uff0c\u63a8\u7406\u6642\u518d\u5f9e\u4e2d\u627e</td></tr><tr><td colspan=\"2\">\u51fa\u8207\u554f\u984c\u95dc\u806f\u6027\u9ad8\u7684\u8a18\u61b6\u5167\u5bb9\uff0c\u53ef\u4ee5\u907f\u514d\u5728\u8a08\u7b97\u904e\u7a0b\u4e2d\u640d\u5931\u91cd\u8981\u7684\u8cc7\u8a0a\u3002\u5176\u4e2d\u7d50\u5408\u4e86\u6ce8</td></tr><tr><td colspan=\"2\">\u610f\u529b\u6a5f\u5236\u7684\u61c9\u7528\uff0c\u4f7f\u8f38\u51fa\u6a21\u7d44\u53ef\u4ee5\u6839\u64da\u73fe\u5728\u7684\u554f\u984c\u95dc\u6ce8\u91cd\u8981\u7684\u8a18\u61b6\u5167\u5bb9\uff0c\u63a8\u7406\u5f97\u51fa\u6b63\u78ba</td></tr><tr><td>\u7684\u7b54\u6848\u3002</td><td/></tr><tr><td colspan=\"2\">\u61c9\u7528\u65bc\u8a9e\u8a00\u6a21\u578b\u9818\u57df\u4e2d\u7684\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u6709\u55ae\u8df3\u8e8d\u6ce8\u610f\u6a5f\u5236(Single-hop attention) \u8207</td></tr><tr><td colspan=\"2\">\u591a\u8df3\u8e8d\u6ce8\u610f\u6a5f\u5236(Multi-hop attention) \u7684\u63a8\u7406\u65b9\u5f0f\uff0c\u56e0\u8a18\u61b6\u8207\u8a18\u61b6\u9593\u76f8\u4e92\u7368\u7acb\uff0c\u5728\u6578\u64da\u91cf</td></tr><tr><td colspan=\"2\">\u8db3\u5920\u5927\u7684\u72c0\u6cc1\u4e0b\u5df2\u53ef\u5b78\u7fd2\u5230\u4e0d\u932f\u7684\u6548\u679c\u3002\u4f46\u5728\u6578\u64da\u91cf\u8f03\u5c0f\u7684\u524d\u63d0\u4e0b\u5247\u8f03\u70ba\u7121\u6cd5\u5b78\u7fd2\u6578\u64da</td></tr><tr><td colspan=\"2\">\u5167\u7684\u8cc7\u8a0a\u3002\u70ba\u63d0\u9ad8\u6a21\u578b\u5b78\u7fd2\u8207\u63a8\u7406\u7684\u80fd\u529b\uff0c\u63d0\u9ad8\u8a18\u61b6\u5132\u5b58\u6548\u7387\u8207\u6a21\u578b\u7684\u63a8\u7406\u65b9\u6cd5\u5247\u986f\u5f97</td></tr><tr><td>\u76f8\u7576\u91cd\u8981\u3002</td><td/></tr><tr><td colspan=\"2\">\u672c\u7814\u7a76\u7d50\u5408\u81ea\u7136\u8a9e\u8a00\u5e38\u7528\u8cc7\u6599\u96c6\u8207\u6df1\u5ea6\u5b78\u7fd2\u7684\u5de5\u5177\uff0c\u5617\u8a66\u7d50\u5408\u4e0d\u540c\u7406\u8ad6\uff0c\u5f37\u5316\u8a9e\u8a00</td></tr><tr><td colspan=\"2\">\u7406\u89e3\u8207\u63a8\u7406\u80fd\u529b\uff0c\u4e26\u5206\u6790\u5be6\u9a57\u7d50\u679c\u7684\u8868\u73fe\u3002\u5be6\u9a57\u63a1\u53d6\u8f03\u5c0f\u7684\u6578\u64da\u91cf\u4f5c\u70ba\u9a57\u8b49\u76ee\u6a19\uff0c\u6539\u5584</td></tr><tr><td>\u5373\u4f7f\u5728\u6578\u64da\u96c6\u4e0d\u8db3\u7684\u60c5\u6cc1\u4e5f\u80fd\u9054\u5230\u76f8\u7576\u7a0b\u5ea6\u7684\u6539\u5584\u6548\u679c\u3002\u76ee\u7684\u6b78\u7d0d\u5982\u4e0b\u5169\u9ede\u3002</td><td/></tr><tr><td>(\u4e00) \u7814\u7a76\u591a\u8df3\u8e8d\u6ce8\u610f\u6a5f\u5236\u5c0d\u65bc\u8a18\u61b6\u7db2\u8def\u9810\u6e2c\u7684\u8868\u73fe\u3002</td><td/></tr><tr><td>(\u4e8c) \u7814\u7a76\u8a18\u61b6\u7db2\u8def\u8a18\u61b6\u95dc\u806f\u63d0\u53d6\u5c0d\u65bc\u63a8\u7406\u80fd\u529b\u7684\u63d0\u5347\u3002</td><td/></tr><tr><td colspan=\"2\">\u672c\u8ad6\u6587\u7814\u7a76\u5728\u5c0f\u6578\u64da\u96c6\u7684\u524d\u63d0\u4e0b\uff0c\u4e0d\u540c\u7684\u6a5f\u5236\u5c0d\u65bc\u554f\u7b54\u7cfb\u7d71\u6a21\u578b\u7684\u5f71\u97ff\u3002\u5728\u7814\u7a76\u4e2d</td></tr><tr><td colspan=\"2\">\u6211\u5011\u5c07\u95dc\u4fc2\u7db2\u8def\u7684\u6982\u5ff5\uff0c\u4ee5\u95dc\u806f\u8a18\u61b6\u7684\u5f62\u5f0f\u8207\u8a18\u61b6\u6a21\u578b\u7d50\u5408\uff0c\u65bc bAbI \u6578\u64da\u96c6(Weston,</td></tr></table>", |
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| "text": "Bordes, Chopra & Mikolov, 2016)20 \u9805\u4efb\u52d9\u4e2d\u9a57\u8b49\uff0c\u6e96\u78ba\u7387\u6700\u591a\u53ef\u63d0\u9ad8\u7d04 9.2%\u5de6\u53f3\u7684\u6e96\u78ba \u7387\u3002\u95dc\u806f\u63d0\u53d6\u7684\u90e8\u5206\u9084\u6709\u964d\u4f4e\u6b0a\u91cd\u7684\u529f\u7528\uff0c\u76f8\u6bd4\u65bc\u4fdd\u5b58\u6240\u6709\u95dc\u806f\u8a08\u7b97\uff0c\u5e73\u5747\u6bcf\u9805\u4efb\u52d9\u53ef \u4e0b\u964d 3 \u842c\u500b\u6b0a\u91cd\u6578\u91cf\uff0c\u6574\u9ad4\u4e0b\u964d 26.8%\u6b0a\u91cd\u7684\u8a08\u7b97\u91cf\u3002", |
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| "content": "<table><tr><td>\u8a18\u61b6\u7db2\u8def(Memory Network, MemNN) (Weston, Chopra & Bordes, 2014)\u7531 Facebook \u4eba\u5de5\u667a</td></tr><tr><td>\u6167\u5be6\u9a57\u5ba4\u6240\u63d0\u51fa\uff0c\u76ee\u7684\u5728\u63d0\u9ad8\u985e\u795e\u7d93\u7db2\u8def\u6a21\u578b\u9577\u671f\u8a18\u61b6\u80fd\u529b\uff0c\u61c9\u7528\u65bc\u5e8f\u5217\u6027\u8cc7\u6599\u4e0a\u3002\u5982</td></tr><tr><td>\u4fdd\u5b58\u554f\u7b54\u4efb\u52d9\u7684\u5148\u9a57\u77e5\u8b58\u3001\u804a\u5929\u7684\u8a9e\u5883\u8a0a\u606f\u7b49\u3002\u904e\u5f80\u5728\u8655\u7406\u5e8f\u5217\u6027\u8cc7\u6599\u6642\uff0cRNN \u53ef\u4ee5\u6709</td></tr><tr><td>\u6548\u7684\u8655\u7406\u77ed\u671f\u6642\u9593\u5148\u5f8c\u95dc\u4fc2\uff0c\u6bcf\u500b\u6642\u9593\u6b65\u90fd\u6703\u53c3\u8003\u4e0a\u4e00\u500b\u6642\u9593\u6b65\u8f38\u51fa\u7684\u7d50\u679c\uff0c\u4f46\u5176\u53ea\u900f</td></tr><tr><td>\u904e\u8a18\u61b6\u55ae\u5143\u5132\u5b58\u91cd\u8981\u8cc7\u8a0a\uff0c\u96a8\u6642\u9593\u6b65\u63a8\u79fb\u66f4\u65b0\u8a18\u61b6\u55ae\u5143\u5167\u5bb9\uff0c\u5c0d\u65bc\u9577\u5e8f\u5217\u7684\u8a13\u7df4\u904e\u7a0b\u4e2d</td></tr><tr><td>\u53ef\u80fd\u6703\u6709\u68af\u5ea6\u6d88\u5931(gradient vanishing) \u8207\u68af\u5ea6\u7206\u70b8(gradient exploding) \u7684\u554f\u984c\u767c\u751f\uff0c\u9020\u6210</td></tr><tr><td>RNN \u5728\u9577\u671f\u8a18\u61b6\u4e2d\u8868\u73fe\u4e0d\u662f\u5f88\u597d\u3002\u5373\u4f7f\u5f8c\u4f86\u9577\u77ed\u671f\u8a18\u61b6\u6a21\u578b(Long Short-Term Memory,</td></tr><tr><td>LSTM)</td></tr></table>", |
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| "content": "<table><tr><td>120</td><td>\u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76 \u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76 \u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76 \u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76 \u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76 \u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76 \u61c9\u7528\u591a\u8df3\u8e8d\u6ce8\u610f\u8a18\u61b6\u95dc\u806f\u65bc\u8a18\u61b6\u7db2\u8def\u4e4b\u7814\u7a76</td><td>107 \u8a79\u4eac\u7ff0 \u7b49 109 \u8a79\u4eac\u7ff0 \u7b49 \u8a79\u4eac\u7ff0 \u7b49 113 \u8a79\u4eac\u7ff0 \u7b49 115 \u8a79\u4eac\u7ff0 \u7b49 117 \u8a79\u4eac\u7ff0 \u7b49 119 \u8a79\u4eac\u7ff0 \u7b49 121</td></tr><tr><td colspan=\"3\">\uff0c \u0303 \u2190 \u5728\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u7684\u57fa\u790e\u4e0a\u4fee\u6539\u8207\u5b8c\u5584\uff0c\u4f7f\u5176\u80fd\u4ee5\u7aef\u5c0d\u7aef\u7684\u65b9\u5f0f\u5b8c\u6210\u5b78\u7fd2\u3002\u900f\u904e\u5f31\u76e3\u7763\u65b9 \u5f0f(Weak-Supervise Learning) \u5373\u53ef\u5b8c\u6210\u8a13\u7df4\uff0c\u6709\u5229\u6a21\u578b\u7684\u64f4\u5c55\u4e26\u61c9\u7528\u5230\u4e0d\u540c\u7684\u4efb\u52d9\u6216\u8cc7 \u6599\u96c6\u4e0a\u3002\u6b64\u6a21\u578b\u5229\u7528\u8edf\u6027\u6ce8\u610f\u529b\u6a5f\u5236(Soft Attention Mechanism) \u4f86\u4f30\u8a08\u6bcf\u689d\u8a18\u61b6\u8207\u554f\u984c \u76f8\u95dc\u7684\u7a0b\u5ea6\uff0c\u4e26\u4f7f\u7528\u76f8\u95dc\u6027\u9ad8\u7684\u8a18\u61b6\u8a08\u7b97\u51fa\u6700\u5f8c\u7684\u8f38\u51fa\u3002 \u52d5\u614b\u8a18\u61b6\u7db2\u8def(Dynamic Memory Networks, DMN)\u6a21\u578b(Kumar et al., 2016)\uff0c\u5c07\u5927\u90e8\u4efd \u81ea\u7136\u8a9e\u8a00\u8655\u7406\u9818\u57df\u7684\u4efb\u52d9\u8996\u70ba\u554f\u7b54\u4efb\u52d9\u7684\u4e00\u7a2e\u3002\u8a72\u6a21\u578b\u4ee5\u8a18\u61b6\u7db2\u8def\u70ba\u57fa\u790e\u9032\u884c\u6539\u5584\uff0c\u53ef \u900f\u904e\u7aef\u5c0d\u7aef\u5b78\u7fd2\u5b8c\u6210\u8a13\u7df4\uff0c\u61c9\u7528\u65bc\u554f\u7b54\u4efb\u52d9\u3001\u60c5\u611f\u5206\u6790\u4ee5\u53ca\u8a5e\u6027\u6a19\u8a3b\u7b49\u3002\u6a21\u578b\u67b6\u69cb\u8207\u8a18 \u61b6\u7db2\u8def\u6a21\u578b\u76f8\u4f3c\uff0c\u4e3b\u8981\u7531\u56db\u500b\u6a21\u7d44\u6240\u7d44\u6210\uff0c\u5206\u5225\u70ba\u8f38\u5165\u6a21\u7d44\u3001\u554f\u984c\u6a21\u7d44\u3001\u60c5\u666f\u8a18\u61b6\u6a21\u7d44 (Episodic Memory Module) \u8207\u61c9\u7b54\u6a21\u7d44\u3002\u8207\u524d\u8ff0\u8a18\u61b6\u7db2\u8def\u7684\u4e0d\u540c\u5728\u65bc\u7de8\u78bc\u65b9\u5f0f\u3002\u6b64\u6a21\u578b \u63a1\u7528\u9580\u63a7\u5faa\u74b0\u55ae\u5143\u6a21\u578b(Gate Recurrent Unit, GRU) (Chung, Gulcehre, Cho & Bengio, 2014) \u7de8\u78bc\uff0c\u96a8\u8457\u6642\u9593\u6b65\u7684\u63a8\u79fb\u66f4\u65b0\u96b1\u85cf\u72c0\u614b\u3002\u76f8\u8f03\u65bc\u55ae\u7d14\u4f7f\u7528\u8a5e\u888b(Bags of word, BOW) \u66f4\u53ef \u4ee5\u8868\u793a\u51fa\u5b57\u8a5e\u4e4b\u9593\u7684\u9806\u5e8f\u95dc\u806f\u3002 \u5728\u554f\u7b54\u7cfb\u7d71\u4e2d\u52a0\u5165\u77e5\u8b58\u5eab(Knowledge Bases, KBs)\u53ef\u4ee5\u6709\u6548\u7684\u63d0\u9ad8\u6a21\u578b\u7684\u77e5\u8b58\u5132\u5b58 \u91cf\uff0c\u4f46\u5176\u4e26\u4e0d\u5920\u5b8c\u6574\uff0c\u7121\u6cd5\u652f\u6301\u4e0d\u540c\u985e\u578b\u7684\u7b54\u6848\uff0c\u7531\u65bc\u6578\u64da\u7684\u7a00\u758f\u6027\uff0c\u8f03\u96e3\u5275\u5efa\u5305\u542b\u6240 \u6709\u9818\u57df\u7684 KB\uff0c\u4e0d\u5229\u65bc\u64f4\u5c55\u5230\u4e0d\u540c\u7684\u9818\u57df\u3002\u9375\u503c\u8a18\u61b6\u7db2\u8def(Key-Value Memory Networks) \u6a21\u578b(Miller et al., 2016)\u4f7f\u7528\u9375\u503c(key-value) \u7684\u65b9\u5f0f\u5c07\u6587\u7ae0\u4e2d\u7684\u7de8\u78bc\u5b58\u53d6\u4e0b\u4f86\uff0c\u67b6\u69cb\u57fa\u65bc \u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u6a21\u578b\uff0c\u91dd\u5c0d\u5148\u9a57\u77e5\u8b58\u7684\u5132\u5b58\u63d0\u51fa\u4e0d\u540c\u65b9\u5f0f\u7de8\u78bc\uff0c\u61c9\u7528\u65bc\u81ea\u7136\u8a9e\u8a00\u4e2d\u554f\u7b54 \u7684\u76f8\u95dc\u9818\u57df\u3002 \u9375\u503c\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u8207\u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u76f8\u4f3c\uff0c\u6700\u5927\u7684\u4e0d\u540c\u5728\u65bc\u8a18\u61b6\u7684\u5132\u5b58\u65b9\u5f0f\u3002 \u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u900f\u904e\u4e0d\u540c\u7684\u5d4c\u5165\u77e9\u9663\u5c0d\u6587\u672c\u7de8\u78bc\uff0c\u800c\u9375\u503c\u8a18\u61b6\u7db2\u8def\u5247\u900f\u904e\u9375\u503c\u7684\u65b9\u5f0f\u8868 \u793a\uff0c\u4ee5\u9375\u8a18\u61b6(key memory)\u8207\u503c\u8a18\u61b6(value memory)\u5f62\u5f0f\u5132\u5b58\u3002\u9375\u503c\u8a18\u61b6\u7db2\u8def\u512a\u9ede\u70ba\u5728\u8a13 \u7df4\u7db2\u8def\u4e4b\u524d\uff0c\u53ef\u5148\u5c0d\u5148\u9a57\u77e5\u8b58\u9032\u884c\u9069\u5408\u7684\u7de8\u78bc\u3002\u5373\u4f7f\u662f\u4e0d\u540c\u9818\u57df\u7684\u77e5\u8b58\uff0c\u4f7f\u7528\u8005\u4e5f\u53ef\u9078 \u64c7\u7de8\u78bc\u65b9\u5f0f\uff0c\u800c\u4e0d\u55ae\u7d14\u4f9d\u8cf4\u65bc\u8a5e\u5d4c\u5165\u77e9\u9663\u7684\u8a13\u7df4\uff0c\u5728\u4f7f\u7528\u4e0a\u6709\u4e86\u66f4\u591a\u7684\u5f48\u6027\u3002 \u905e\u6b78\u5be6\u9ad4\u7db2\u8def(Recurrent Entity Networks, EntNet)\u6a21\u578b(Henaff, Weston, Szlam, Bordes & LeCun, 2017)\uff0c\u8a18\u9304\u4e16\u754c\u7684\u5be6\u9ad4\u8207\u72c0\u614b\u65bc\u8a18\u61b6\u4e2d\uff0c\u7576\u6709\u65b0\u8cc7\u8a0a\u8f38\u5165\u6642\uff0c\u5247\u6839\u64da\u8f38\u5165\u8a0a \u606f\u66f4\u65b0\u76f8\u5c0d\u61c9\u8a18\u61b6\u55ae\u5143\uff0c\u53ef\u61c9\u7528\u65bc\u81ea\u7136\u8a9e\u8a00\u4e2d\u7684\u95b1\u8b80\u7406\u89e3\u8207\u554f\u7b54\u4efb\u52d9\u4e2d\uff0c\u5176\u5728 bAbI-10k \u6578\u64da\u96c6\u8207 Children'sBook Test (CBT)\u6578\u64da\u96c6 single hop \u8a13\u7df4\u4e2d\uff0c\u8f03\u66f4\u65e9\u63d0\u51fa\u4e4b\u65b9\u6cd5\u8868\u73fe\u70ba \u512a\u3002 \u6211\u5011\u4f9d\u7167\u8ad6\u6587(Sukhbaatar et al., 2015) (Henaff et al., 2017)\u4e2d\u4e4b\u65b9\u6cd5\uff0c\u4ee5\u4e00\u5343\u7b46\u8a13\u7df4\u8cc7 \u6599\u9032\u884c\u5be6\u9a57\u767c\u73fe\uff0c\u6700\u521d\u8a18\u61b6\u6a21\u578b\u6709\u6548\u6539\u5584\u9577\u671f\u8a18\u61b6\u95dc\u4fc2\uff0c\u53ef\u5728 bAbI \u8cc7\u6599\u96c6\u4e2d\u901a\u904e 16/20 \u9805\u4efb\u52d9\u3002\u4f46\u9700\u8981\u900f\u904e\u5f37\u76e3\u7763\u65b9\u5f0f\u9032\u884c\u8a13\u7df4\uff0c\u4e26\u4e0d\u5229\u65bc\u64f4\u5c55\u61c9\u7528\u3002\u800c\u5f31\u76e3\u7763\u8a13\u7df4\u5247\u53ea\u80fd\u901a \u904e 2/20 \u9805\u4efb\u52d9\uff0c\u4e14\u932f\u8aa4\u7387\u5927\u5e45\u589e\u9577\u3002\u800c\u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u900f\u904e\u5f31\u76e3\u7763\u65b9\u5f0f\u8a13\u7df4\uff0c\u76f8\u8f03 \u65bc\u5f31\u76e3\u7763\u8a18\u61b6\u7db2\u8def\uff0c\u901a\u904e\u4efb\u52d9\u6bd4\u4f8b\u63d0\u6607\uff0c\u4e5f\u5927\u5e45\u4e0b\u964d\u5e73\u5747\u932f\u8aa4\u7387\u3002 \u800c\u7d9c\u5408\u524d\u8ff0\u8ad6\u6587\u6240\u63d0\u4f9b\u7684\u6578\u64da\uff0c\u4ee5\u4e00\u842c\u7b46\u8a13\u7df4\u8cc7\u6599\u70ba\u524d\u63d0\u5be6\u9a57\uff0c\u76f8\u8f03\u65bc\u524d\u8ff0\u4ee5\u4e00\u5343 \u7b46\u8cc7\u6599\u8a13\u7df4\uff0c\u6a21\u578b\u76f8\u5c0d\u901a\u904e\u66f4\u591a\u7684\u4efb\u52d9\u3002\u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u901a\u904e\u4e86 17/20 \u9805\u4efb\u52d9\uff1b\u52d5 \u614b\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u901a\u904e\u4e86 18/20 \u9805\u4efb\u52d9\u3002\u800c\u9996\u5148\u901a\u904e\u6240\u6709\u4efb\u52d9\u7684\u6a21\u578b\u70ba\u905e\u6b78\u5be6\u9ad4\u7db2\u8def\u6a21\u578b\uff0c \u5176\u5e73\u5747\u932f\u8aa4\u7387\u964d\u4f4e\u81f3 0.54%\u3002\u5982\u8868 1 \u6240\u793a\u3002 \u8868 1Model MemNN MemN2N DMN EntNet Mean Error 39.2 4.2 6.395 0.54 Failed Tasks(error>5%) 17 3 2 0 \u96d6\u7136\u5728\u8868 1 \u4e2d EntNet \u5728 10k \u6578\u64da\u91cf\uff0c\u932f\u8aa4\u7387\u5c0f\u65bc 5%\u7684\u6a19\u6e96\u4e2d\u901a\u904e\u6240\u6709\u4efb\u52d9\uff0c\u4f46\u5176 \u5728\u90e8\u4efd\u4efb\u52d9\u4e2d\u7684\u932f\u8aa4\u7387\u9084\u662f\u5927\u65bc 4%\u3002\u800c\u4e14\u82e5\u662f\u5229\u7528\u8f03\u5c11\u7684 1k \u7684\u6578\u64da\u91cf\u9032\u884c\u8a13\u7df4\u7684\u8a71\uff0c \u6b63\u78ba\u5ea6\u5247\u6703\u5f9e\u539f\u672c\u7684 99.5%\u964d\u5230 89.1%\u3002\u56e0\u6b64\u5728\u8f03\u5c11\u6578\u64da\u7684\u60c5\u6cc1\u4e0b\uff0c\u6a21\u578b\u7684\u6e96\u78ba\u6027\u9084\u6709 \u53ef\u4ee5\u6539\u9032\u7684\u7a7a\u9593\u3002\u82e5\u662f\u6a21\u578b\u80fd\u63d0\u9ad8\u5c11\u6578\u64da\u4e0b\u8a13\u7df4\u7684\u6548\u679c\uff0c\u53ef\u4ee5\u6e1b\u5c11\u8a13\u7df4\u6642\u9593\uff0c\u8207\u8cc7\u6599\u6536 \u96c6\u7684\u6210\u672c\u3002\u800c\u82e5\u662f\u8981\u61c9\u7528\u5230\u5176\u4ed6\u7684\u8cc7\u6599\u91cf\u8f03\u5c11\u7684\u60c5\u6cc1\uff0c\u4e5f\u80fd\u6709\u6bd4\u8f03\u597d\u7684\u6548\u679c\u3002 2.3 \u591a\u8df3\u8e8d\u6ce8\u610f(Multi-hop Attention)\u6a5f\u5236\u65bc\u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u4e2d\u6240\u63d0\u51fa\uff0c\u900f\u904e\u4e0d\u65b7\u6bd4\u5c0d\u554f \u984c\u8207\u8a18\u61b6\u5f97\u51fa\u554f\u984c\u7b54\u6848\uff0c\u9019\u500b\u904e\u7a0b\u6a21\u64ec\u4eba\u985e\u5728\u63a8\u7406\u904e\u7a0b\u7684\u601d\u8003\u65b9\u5f0f\uff0c\u5f8c\u7e8c\u6a21\u578b\u900f\u904e\u4e0d\u540c \u7684\u65b9\u5f0f\u5be6\u73fe\u591a\u8df3\u8e8d\u6a5f\u5236\uff0c\u7528\u4ee5\u5f37\u5316\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\u3002 \u554f \u984c \u7c21 \u5316 \u7db2 \u8def \u6a21 \u578b (Question Reduction Networks, QRN) (Seo, Min, Farhadi & Hajishirzi, 2017) \u6a21\u578b\u67b6\u69cb\u70ba RNN \u7684\u4e00\u7a2e\uff0c\u53ef\u6709\u6548\u8655\u7406\u77ed\u671f\u8207\u9577\u671f\u5e8f\u5217\u95dc\u4fc2\u3002\u900f\u904e\u591a\u8f2a \u8b80\u53d6\u6a5f\u5236\uff0c\u9010\u6f38\u7c21\u5316\u554f\u984c\uff0c\u9054\u5230\u6df1\u5165\u8a9e\u610f\u7406\u89e3\u7684\u6548\u679c\uff0c\u6700\u5f8c\u63a8\u7406\u5f97\u51fa\u6700\u7d42\u7b54\u6848\u4e26\u8f49\u5316\u70ba \u81ea\u7136\u8a9e\u8a00\u8f38\u51fa\u3002\u6b64\u5916 QRN \u6a21\u578b\u4e2d\u6240\u63d0\u51fa\u7684\u516c\u5f0f\u5141\u8a31\u5728\u905e\u6b78\u795e\u7d93\u7db2\u8def\u6642\u9593\u8ef8\u4e0a\u4e26\u884c\u5316\uff0c \u63d0\u5347\u8a13\u7df4\u8207\u63a8\u7406\u90e8\u5206\u7684\u6548\u7387\u3002 AOA Reader \u6a21\u578b(Attention-over-Attention) (Cui et al., 2017)\uff0c\u61c9\u7528\u65bc\u586b\u7a7a\u4efb\u52d9 (Cloze-style questions)\u3002\u8207\u904e\u5f80\u6a21\u578b\u6700\u5927\u7684\u4e0d\u4e00\u6a23\u5728\u65bc\u900f\u904e\u4e0d\u4e00\u6a23\u7684\u6ce8\u610f\u529b\u6a5f\u5236\u7d44\u5408\uff0c\u8a08 \u7b97\u51fa\u6b0a\u91cd\u9810\u6e2c\u6700\u5f8c\u7684\u7d50\u679c\uff0c\u800c\u975e\u4f7f\u7528\u55ae\u4e00\u4e00\u7a2e\u6ce8\u610f\u529b\u6a5f\u5236\u8a08\u7b97\u65b9\u6cd5\u3002\u6a21\u578b\u900f\u904e\u96d9\u5411\u9580\u63a7 \u5faa\u74b0\u6a21\u578b\u5c0d\u5148\u9a57\u77e5\u8b58\u8207\u554f\u984c\u9032\u884c\u7de8\u78bc\uff0c\u5c07\u7de8\u78bc\u5411\u91cf\u9ede\u7a4d\u76f8\u4e58\uff0c\u7d93\u904e softmax \u8a08\u7b97\u51fa\u8a5e\u5f59 \u7684\u6a5f\u7387\uff0c\u6b64\u6ce8\u610f\u529b\u6a5f\u5236\u7684\u8a08\u7b97\u65b9\u6cd5\u70ba\u8a31\u591a\u6a21\u578b\u901a\u7528\u65b9\u6cd5\uff0c\u800c\u6b64\u8ad6\u6587\u5275\u65b0\u7684\u5730\u65b9\u70ba\u5176\u4e0d\u50c5 \u8a08\u7b97 document-to-query \u7684\u6ce8\u610f\u529b\u6578\u503c\uff0c\u4e5f\u8a08\u7b97 query-to-document \u7684\u6ce8\u610f\u529b\u6b0a\u91cd\uff0c\u6700\u5f8c\u5229 \u7528\u5169\u8005\u77e9\u9663\u76f8\u4e58\u5f97\u5230\u6700\u5f8c\u6ce8\u610f\u529b\u6a5f\u5236\u6578\u503c\uff0c\u4e26\u900f\u904e\u5f8c\u7e8c\u6a21\u578b\u9032\u884c\u63a8\u7406\u3002 \u8ad6\u6587(Trischler et al., 2016)\u63d0\u51fa\u4e86 EpiReader \u795e\u7d93\u7db2\u8def\u6a21\u578b\uff0c\u7528\u4ee5\u89e3\u6c7a\u81ea\u7136\u8a9e\u8a00\u4efb\u52d9 \u4e2d\u7684\u586b\u7a7a\u554f\u984c\u3002EpiReader \u6a21\u578b\u5206\u70ba\u5169\u500b\u90e8\u5206\uff0c\u7b2c\u4e00\u90e8\u5206\u70ba\u63d0\u53d6\u6a21\u7d44(Extractor)\uff0c\u901a\u904e\u6dfa \u5c64 \u6587\u672c \u8207\u554f\u984c \u7684\u6bd4 \u5c0d\uff0c\u63d0 \u53d6\u51fa \u82e5\u5e72\u500b \u554f\u984c \u7684\u53ef\u80fd \u5019\u9078 \u7b54\u6848\uff1b \u7b2c\u4e8c \u90e8\u5206\u70ba \u63a8\u7406 \u6a21\u7d44 (Reasoner)\uff0c\u901a\u904e\u66f4\u6df1\u5c64\u7684\u8a9e\u610f\u6bd4\u8f03\u5019\u9078\u7b54\u6848\u8207\u554f\u984c\u4e4b\u9593\u7684\u95dc\u806f\u3002\u63d0\u53d6\u6a21\u7d44\u5f9e\u5927\u91cf\u53ef\u80fd\u6027 \u4e2d\u7be9\u9078\u51fa\u5c0f\u90e8\u5206\u5019\u9078\u7b54\u6848\uff0c\u800c\u63a8\u7406\u6a21\u7d44\u5247\u8655\u7406\u66f4\u7cbe\u78ba\u7684\u63a8\u7406\u5339\u914d\u90e8\u5206\u3002 \u795e\u7d93\u8a9e\u610f\u7de8\u78bc\u5668\u6a21\u578b(Model MemN2N QRN 1 hop 2 hop 3 hop 2r 3r Mean Error 9.58 8.45 8.15 9.9 11.3 Failed Tasks(error>5%) 17 11 11 7 5 \u4ee5\u5152\u7ae5\u5716\u66f8\u6e2c\u8a66\u6578\u64da(Children's Book Test, CBT)\u70ba\u5be6\u9a57\u6578\u64da\uff0c\u8868 3 \u6574\u7406\u5e7e\u7a2e\u55ae\u8df3\u8e8d \u8207\u591a\u8df3\u8e8d\u6a21\u578b\u5be6\u9a57\u7d50\u679c\uff0c\u76ee\u524d\u6548\u679c\u6700\u512a\u70ba\u591a\u8df3\u8e8d\u6a21\u578b\uff0c\u56e0\u6b64\u591a\u8df3\u8e8d\u6a5f\u5236\u6210\u70ba\u76ee\u524d\u7814\u7a76\u9818 \u57df\u7684\u8da8\u52e2\uff0c\u800c\u672c\u8ad6\u6587\u7814\u7a76\u4e5f\u5c07\u5617\u8a66\u4ee5\u4e0d\u540c\u65b9\u5f0f\u5c07\u591a\u8df3\u8e8d\u6ce8\u610f\u6a5f\u5236\u7d50\u5408\u55ae\u8df3\u8e8d\u6a21\u578b\u3002 \u8868 3. Single Pass Kneser-Ney Language Model + cache 0.439 0.577 LSTMs (context+query) 0.418 0.560 Window LSTM 0.436 0.582 EntNet (general) 0.484 0.540 EntNet (simple) 0.616 0.588 Multi Pass MemNN 0.493 0.554 MemNN+self-sup 0.666 0.630 EpiReader 0.697 0.674 AoA Reader 0.720 0.694 NSE 0.732 0.714 2.4 \u95dc\u4fc2\u7db2\u8def (Relation Network) \u95dc\u4fc2\u7db2\u8def(Relation Network) (Santoro et al., 2017)\u76ee\u7684\u5728\u65bc\u900f\u904e\u52a0\u5165\u5c0d\u7269\u4ef6\u3001\u5be6\u9ad4\u6216\u662f\u8a9e \u53e5\u4e4b\u9593\u7684\u95dc\u4fc2\u8a08\u7b97\uff0c\u63d0\u4f9b\u66f4\u591a\u8a0a\u606f\u7d66\u5f8c\u7e8c\u63a8\u7406\u6a21\u7d44\u9032\u884c\u63a8\u7406\u3002\u95dc\u4fc2\u7db2\u8def\u61c9\u7528\u65bc\u5716\u50cf\u554f\u7b54 (Visual Question Answering, VQA)\uff0c\u4f7f\u7528\u7c21\u55ae\u7684\u6a21\u578b\u4f86\u5efa\u69cb\u7269\u9ad4\u4e4b\u9593\u7684\u806f\u7e6b\uff0c\u6838\u5fc3\u6982\u5ff5\u5728 \u65bc\u6700\u7d42\u7b54\u6848\u8207\u6210\u5c0d\u7684\u5c0d\u8c61\u5177\u6709\u4e00\u5b9a\u7684\u95dc\u806f\u6027\uff0c\u800c\u554f\u984c\u4e5f\u6703\u5f71\u97ff\u5c0d\u6210\u5c0d\u5c0d\u8c61\u7684\u67e5\u8a62\u3002\u5176\u900f \u904e\u795e\u7d93\u7db2\u8def\u8a08\u7b97\u4efb\u610f\u5c0d\u8c61\u5169\u5169\u4e4b\u9593\u7684\u6f5b\u5728\u95dc\u4fc2\u3002 \u95dc\u4fc2\u7db2\u8def\u7684\u512a\u52e2\u5728\u65bc\u5176\u67b6\u69cb\u7c21\u55ae\uff0c\u4f7f\u7528\u5f48\u6027\u5927\uff0c\u53ef\u5c07\u5176\u63d2\u5165\u65bc\u4e0d\u540c\u7684\u6a21\u578b\u88e1\uff0c\u63d0\u9ad8 \u4e86\u6a21\u578b\u63a8\u7406\u7684\u80fd\u529b\uff0c\u53ef\u61c9\u7528\u65bc\u95dc\u4fc2\u63a8\u7406\u76f8\u95dc\u4efb\u52d9\u4e0a\u3002\u524d\u8ff0\u8ad6\u6587\u5728\u5be6\u9a57\u4e2d\u4f7f\u7528\u554f\u7b54\u76f8\u95dc\u8cc7 \u6599\u96c6\u505a\u70ba\u9a57\u8b49\uff0c\u5728 bAbI dataset \u4e8c\u5341\u500b\u4efb\u52d9\u4e2d\u901a\u904e\u4e86\u5341\u516b\u500b\uff0c\u800c\u5728 Sort-of-CLEVR \u4e2d\u53d6\u5f97 \u6700\u512a\u7684\u7d50\u679c\uff0c\u4e14\u8d85\u904e\u4eba\u985e\u6240\u80fd\u9054\u5230\u7684\u5206\u6578\u3002 RelNet (Bansal, Neelakantan & McCallum, 2017)\u4e2d\u5c07\u8a08\u7b97\u5169\u5169\u7269\u4ef6\u4e4b\u9593\u95dc\u4fc2\u7684\u6982\u5ff5 \u5e36\u5165\u905e\u6b78\u5be6\u9ad4\u7db2\u8def\u4e2d\uff0c\u6b64\u8ad6\u6587\u5c07\u95dc\u4fc2\u6982\u5ff5\u7528\u4f86\u8a08\u7b97\u8a18\u61b6\u8207\u8a18\u61b6\u4e4b\u9593\u7684\u95dc\u806f\u3002\u904e\u5f80\u905e\u6b78\u5be6 \u9ad4\u7db2\u8def\u6a21\u578b\u8207\u8a18\u61b6\u7db2\u8def\u76f8\u95dc\u6a21\u578b\uff0c\u8a18\u61b6\u7684\u5132\u5b58\u76f8\u4e92\u4e4b\u9593\u7368\u7acb\uff0c\u800c RelNet \u6a21\u578b\u900f\u904e\u95dc\u4fc2\u8a08 \u7b97\u5c07\u8a18\u61b6\u5f7c\u6b64\u9023\u7d50\u8d77\u4f86\u3002\u8a08\u7b97\u8a18\u61b6\u72c0\u614b\u5132\u5b58\u7684\u516c\u5f0f\u8207\u539f\u6a21\u578b\u76f8\u540c\uff0c\u5dee\u5225\u5728\u591a\u52a0\u4e0a\u4e86\u8a08\u7b97 \u4e0d\u540c\u8a18\u61b6\u4e4b\u9593\u7684\u95dc\u806f\uff0c\u4e26\u61c9\u7528\u65bc\u6700\u5f8c\u7684\u63a8\u7406\u8a08\u7b97\u4e0a\u3002 \u905e\u6b78\u95dc\u4fc2\u7db2\u8def(Recurrent Relational Networks, RRN)\u6a21\u578b(Palm, Paquet & Winther, 2017)\u904b\u7528\u7bc0\u9ede\u95dc\u4fc2\u89e3\u6c7a\u6578\u7368\u7684\u554f\u984c\uff0c\u5728 9*9 \u7684\u6578\u7368\u5167\u5171\u6709 81 \u500b\u7bc0\u9ede\uff0c\u6bcf\u500b\u7bc0\u9ede\u90fd\u9700\u8981 \u8003\u616e\u540c\u4e00\u884c\u3001\u540c\u4e00\u5217\u8207\u540c\u500b\u65b9\u6846\u5167\u7684\u8a0a\u606f\uff0c\u4e0d\u80fd\u51fa\u73fe\u540c\u6a23\u7684\u6578\u5b57\u3002\u6b64\u6a21\u578b\u5c0d\u6bcf\u500b\u7bc0\u9ede\u521d \u59cb\u5316\u7684\u72c0\u614b\u70ba{ 1, 2,\u2026, 81}\uff0c\u900f\u904e\u591a\u5c64\u611f\u77e5\u5668(MLP) \u8a08\u7b97\u6bcf\u500b\u7bc0\u9ede\u4e4b\u9593\u7684\u95dc\u806f\uff0c\u5c07\u8a08 \u7b97\u51fa\u7684\u95dc\u4fc2\u6578\u503c\u76f8\u52a0\uff0c\u7528\u4ee5\u66f4\u65b0\u7d50\u9ede\u7684\u72c0\u614b\uff0c\u6bcf\u500b\u7bc0\u9ede\u7684\u66f4\u65b0\u8003\u616e\u4e0a\u4e00\u500b\u6642\u9593\u6b65\u7684\u72c0\u614b\u3001 \u8f38\u5165\u4ee5\u53ca\u95dc\u4fc2\u6578\u503c\u3002\u6b64\u6a21\u578b\u9664\u4e86\u61c9\u7528\u65bc\u6578\u7368\u4e0a\uff0c\u4e5f\u5728 bAbI \u6578\u64da\u96c6\u3001Pretty-CLEVR1 \u8868\u73fe \u512a\u79c0\u3002 \u4ee5(Method Mean Error Rate (%) RRN 0.46\u00b10.77 RelNet 0.29 EntNet 9.7\u00b12.6 2.5 \u9810\u8a13\u7df4\u6a21\u578b (Pre-trained Model) \u8fd1\u5e74\u4f86\u7684\u8a9e\u8a00\u6a21\u578b\u7814\u7a76\u4f7f\u7528\u5927\u91cf\u6587\u7ae0\u9810\u8a13\u7df4(Pre-train)\u901a\u7528\u8a9e\u8a00\u6a21\u578b\uff0c\u7136\u5f8c\u518d\u6839\u64da\u5177\u9ad4\u61c9 \u7528\uff0c\u7528 supervised \u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u5fae\u8abf(Fine-tuning)\u6a21\u578b\uff0c\u4f7f\u4e4b\u9069\u7528\u4e8e\u5177\u9ad4\u61c9\u7528\uff0c\u4f86\u63d0\u6607\u6a21 \u578b\u7684\u6548\u80fd\u3002 \u5176\u4e2d BERT \u6a21\u578b(Devlin, Chang, Lee & Toutanova, 2018)\u4ee5\u53ca\u5f8c\u7e8c\u767c\u8868\uff0c\u8b93 BERT \u66f4 \u5c0f\uff0c\u8a13\u7df4\u66f4\u5feb\u7684 Albert \u6a21\u578b(Lan et al. 2019)\uff0c\u88ab\u5ee3\u6cdb\u5730\u61c9\u7528\u65bc\u554f\u7b54\u4efb\u52d9\uff0c\u4e14\u5f97\u5230\u76f8\u7576\u512a \u7570\u7684\u6210\u679c\u3002 \u9019\u7a2e\u7d50\u5408\u9810\u8a13\u7df4\u6a21\u578b\u518d\u52a0\u4e0a\u5f8c\u7e8c\u7684\u5fae\u8abf\u8a13\u7df4\u7684\u65b9\u5f0f\uff0c\u53ef\u4ee5\u8b93\u8a31\u591a\u7684\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u4efb \u52d9\u5f97\u5230\u6975\u5927\u5e45\u5ea6\u7684\u6548\u80fd\u63d0\u5347\uff0c\u4e5f\u8b93\u6211\u5011\u53ef\u4ee5\u7528\u66f4\u5c0f\u7684\u8cc7\u6599\u5c31\u80fd\u8a13\u7df4\u51fa\u6975\u597d\u7684\u6548\u679c\u3002 3. \u7814\u7a76\u65b9\u6cd5 (Research Method) \u8a18\u61b6\u7db2\u8def\u900f\u904e\u5916\u90e8\u8a18\u61b6\u7684\u4fdd\u5b58\uff0c\u5f37\u5316\u9577\u671f\u8a18\u61b6\u7684\u80fd\u529b\uff0c\u7d93\u904e\u6ce8\u610f\u529b\u6a5f\u5236\u627e\u5c0b\u8207\u554f\u984c\u76f8\u95dc \u7684\u8a18\u61b6\u69fd\uff0c\u63a8\u7406\u51fa\u5c0d\u61c9\u7684\u7b54\u6848\u3002\u8a18\u61b6\u69fd\u8207\u8a18\u61b6\u69fd\u4e4b\u9593\u76f8\u4e92\u7368\u7acb\u904b\u4f5c\uff0c\u91dd\u5c0d\u4e0d\u540c\u7684\u5be6\u9ad4\u4fdd \u5b58\u76f8\u95dc\u8a0a\u606f\uff0c\u5c0d\u65bc\u9700\u8981\u591a\u9805\u8a18\u61b6\u4ea4\u4e92\u63a8\u7406\u7684\u8907\u96dc\u4efb\u52d9\uff0c\u63a8\u7406\u6a21\u7d44\u8f03\u7121\u6cd5\u4f7f\u7528\u8db3\u5920\u7684\u8a0a\u606f \u8f38\u51fa\u6b63\u78ba\u7b54\u6848\u3002\u672c\u7814\u7a76\u5247\u900f\u904e\u8a18\u61b6\u4e4b\u9593\u7684\u95dc\u806f\u8a08\u7b97\u8207\u591a\u8df3\u8e8d\u6a5f\u5236\u63a8\u7406\uff0c\u5617\u8a66\u63d0\u9ad8\u6a21\u578b\u8a18 \u61b6\u5132\u5b58\u8207\u63a8\u7406\u80fd\u529b\uff0c\u4e26\u4ee5\u554f\u7b54\u76f8\u95dc\u4efb\u52d9\u4f5c\u70ba\u9a57\u8b49\uff0c\u6a21\u578b\u9700\u8981\u5177\u5099\u8a9e\u8a00\u7406\u89e3\u4ee5\u53ca\u63a8\u7406\u7684\u80fd \u529b\u3002\u63a5\u4e0b\u4f86\u6211\u5011\u5c07\u4ecb\u7d39\u672c\u8ad6\u6587\u6574\u9ad4\u6a21\u578b\u7684\u67b6\u69cb\uff0c\u4e26\u4ecb\u7d39\u5404\u6a21\u7d44\u5167\u7684\u8a2d\u8a08\u3002 3.1 \u6a21\u578b\u67b6\u69cb (Model Architecture) \u672c\u8ad6\u6587\u4e4b\u6a21\u578b\u67b6\u69cb\u4ee5 EntNet \u6a21\u578b(Henaff et al., 2017)\u70ba\u57fa\u790e\uff0c\u7528\u56fa\u5b9a\u5927\u5c0f\u8a18\u61b6\u55ae\u5143\u4fdd\u5b58\u8f38 \u5165\u6578\u64da\u7684\u5be6\u9ad4\u8207\u76f8\u95dc\u5c6c\u6027\uff0c\u8a18\u61b6\u5167\u5bb9\u5247\u96a8\u8457\u8f38\u5165\u53e5\u5b50\u5373\u6642\u66f4\u65b0\u3002\u6a21\u578b\u67b6\u69cb\u5728\u8a18\u61b6\u69fd (memory slot)\u4e4b\u9593\u52a0\u4e0a\u8a18\u61b6\u95dc\u806f\u7684\u8a08\u7b97\u8207\u63d0\u53d6\uff0c\u4fdd\u5b58\u65bc\u95dc\u806f\u69fd(relation slot)\u5167\u3002\u539f\u6a21\u578b\u8a18 \u61b6\u69fd\u8207\u8a18\u61b6\u69fd\u4e4b\u9593\u76f8\u4e92\u7368\u7acb\u904b\u4f5c\uff0c\u4f46\u8a18\u61b6\u8207\u8a18\u61b6\u4e4b\u9593\u61c9\u5177\u6709\u4e00\u5b9a\u7684\u95dc\u806f\uff0c\u900f\u904e\u95dc\u806f\u8a08\u7b97 \u53ef\u5c07\u5404\u8a18\u61b6\u69fd\u5167\u5bb9\u806f\u7e6b\u8d77\u4f86\u3002\u6a21\u578b\u4e3b\u8981\u53ef\u5206\u70ba\u4e09\u500b\u90e8\u5206\uff0c\u5206\u5225\u70ba Encoder \u6a21\u7d44\uff0c\u8ca0\u8cac\u5c07 \u8f38\u5165\u7684\u81ea\u7136\u8a9e\u8a00\u8a5e\u53e5\u7de8\u78bc\u70ba\u5411\u91cf\u7684\u5f62\u5f0f\uff0c\u4ee5\u5229\u96fb\u8166\u5f8c\u7e8c\u8a08\u7b97\uff1b\u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u5728\u6bcf\u6b21\u53e5\u5b50 \u8f38\u5165\u6642\uff0c\u66f4\u65b0\u8a18\u61b6\u69fd\u5167\u6240\u5132\u5b58\u7684\u8cc7\u8a0a\uff0c\u518d\u900f\u904e\u8a08\u7b97\u8a18\u61b6\u69fd\u5f7c\u6b64\u9593\u7684\u95dc\u806f\u4f86\u66f4\u65b0\u95dc\u806f\u69fd\uff0c \u8a18\u61b6\u69fd\u5927\u5c0f\u8207\u95dc\u806f\u69fd\u5927\u5c0f\u76f8\u7b49\uff1b\u6700\u5f8c\u8f38\u51fa\u6a21\u7d44\u6839\u64da\u554f\u984c\uff0c\u5f9e\u8a18\u61b6\u69fd\u8207\u95dc\u806f\u69fd\u4e2d\u63a8\u7406\u51fa\u6700 \u5f8c\u7684\u7b54\u6848\u3002\u6574\u9ad4\u67b6\u69cb\u5982\u4e0b\u65b9\u5716 1 \u6a21\u578b\u67b6\u69cb\u5716\u6240\u793a\u3002\u76f8\u8f03 EntNet\uff0c\u5728\u672c\u67b6\u69cb\u4e2d\u6211\u5011\u52a0\u5165\u4e86 Relation memory \u7684\u90e8\u4efd\uff0c\u4ee5\u5617\u8a66\u900f\u904e\u7d50\u5408\u8a18\u61b6\u95dc\u806f\u8a08\u7b97\uff0c\u589e\u52a0\u8a18\u61b6\u9593\u7684\u806f\u7e6b\u3002 \u6b64\u6a21\u578b\u61c9\u7528\u65bc\u554f\u7b54\u4efb\u52d9\u4e0a\uff0c\u6240\u4f7f\u7528\u7684\u6578\u64da\u7686\u70ba\u81ea\u7136\u8a9e\u8a00\u5f62\u5f0f\uff0c\u81ea\u7136\u8a9e\u8a00\u7121\u6cd5\u76f4\u63a5\u8f38\u5165\u81f3 \u96fb\u8166\u8a08\u7b97\uff0c\u56e0\u6b64\u5728\u8f38\u5165\u81f3\u6a21\u578b\u8a13\u7df4\u524d\u9808\u5148\u8f49\u63db\u70ba\u7de8\u78bc\u7684\u5f62\u5f0f\uff0c\u4ee5\u5229\u5f8c\u7e8c\u7684\u904b\u7b97\uff0c\u6b64\u6a21\u7d44 \u5206\u70ba\u5169\u500b\u6b65\u9a5f\u7de8\u78bc\uff0c\u900f\u904e Label encoding \u521d\u6b65\u5c07\u53e5\u5b50\u8f49\u63db\u70ba\u6578\u5b57\uff0c\u518d\u7d93\u904e Position encoding \u7d66\u4e88\u5b57\u8a5e\u65bc\u6574\u9ad4\u53e5\u5b50\u7684\u76f8\u5c0d\u4f4d\u7f6e\u8cc7\u8a0a\u3002\u9996\u5148\u5efa\u7acb\u8a5e\u5f59\u5eab\uff0c\u5c07\u6578\u64da\u96c6\u4e2d\u6240\u6709\u7528\u5230\u7684\u8a5e\u5f59\u5c0d \u61c9\u5230\u4e00\u500b\u56fa\u5b9a\u7684\u6578\u5b57\uff0c\u8a5e\u5f59\u5eab\u4e2d\u8a5e\u5f59\u91cf\u8207\u6578\u5b57\u91cf\u76f8\u7b49\uff0c\u4e0d\u6703\u65b0\u589e\u591a\u65bc\u6b04\u4f4d\u7684\u8a5e\u5f59\u3002\u5b8c\u6210 \u8a5e\u5f59\u5eab\u7684\u5efa\u7acb\u5f8c\uff0c\u5c07\u6578\u64da\u96c6\u6839\u64da\u8a5e\u5f59\u5eab\u8f49\u63db\u70ba\u6578\u5b57\u7684\u5f62\u5f0f\u8868\u793a\u3002\u7bc4\u4f8b\u5982\u4e0b\u6240\u793a:{}\u5167\u70ba\u4e0d \u540c\u8a5e\u5f59\u6240\u5c0d\u61c9\u7684\u7de8\u865f\uff0c[]\u70ba\u6bcf\u500b\u53e5\u5b50\u4f9d\u7167\u8a5e\u5f59\u5eab\u8f49\u63db\u70ba\u5c0d\u61c9\u7de8\u865f\u3002 {hallway:1,John:2,the:3,to:4,went:5,.:6} John went to the hallway.\u2192[2,5,4,3,1,6] \u57fa\u672c\u6578\u503c\u8f49\u63db\u5f8c\uff0c\u96d6\u7136\u53e5\u5b50\u90fd\u4ee5\u6578\u5b57\u5f62\u5f0f\u8868\u793a\uff0c\u4f46\u7de8\u78bc\u4e26\u7121\u76f8\u5c0d\u61c9\u7684\u610f\u7fa9\uff0c\u4ee5\u4e0a\u9762 \u4f8b\u5b50\u70ba\u4f8b\uff0challway \u6578\u503c\u70ba 1\u3001John \u6578\u503c\u70ba 2\uff0challway \u7684\u5169\u500d\u70ba John\uff0c\u9019\u4e26\u7121\u6cd5\u89e3\u91cb\u8a5e \u8207\u8a5e\u4e4b\u9593\u7684\u95dc\u4fc2\uff0c\u6240\u4ee5\u9019\u4e9b\u6578\u5b57\u5c07\u6703\u518d\u6b21\u8f49\u63db\u70ba\u6a21\u578b\u8a13\u7df4\u51fa\u4f86\u7684\u5411\u91cf\uff0c\u800c\u6578\u5b57\u662f\u70ba\u4e86\u5c07 \u76f8\u540c\u7684\u8a5e\u5f59\u8f49\u63db\u70ba\u76f8\u540c\u7684\u5411\u91cf\u3002\u9996\u5148\u6839\u64da\u8a5e\u5f59\u5eab\u7684\u5927\u5c0f\uff0c\u5efa\u7acb\u8207\u8a5e\u5f59\u91cf\u76f8\u7b49\u91cf\u7684\u53ef\u8a13\u7df4 \u5411\u91cf\uff0c\u6bcf\u500b\u8a5e\u5f59\u6709\u5c0d\u61c9\u7684\u5411\u91cf\uff0c\u4e26\u5728\u6574\u9ad4\u6a21\u578b\u8a13\u7df4\u7684\u904e\u7a0b\u4e2d\u4e00\u8d77\u66f4\u65b0\u5411\u91cf\u6578\u503c\uff0c\u900f\u904e\u4f7f \u7528\u81ea\u7136\u8a9e\u8a00\u76f8\u95dc\u6578\u64da\u96c6\uff0c\u8a13\u7df4\u8a5e\u5f59\u5c0d\u61c9\u7684\u5411\u91cf\u3002 \u4f4d\u7f6e\u7de8\u78bc(Position encoding)\u76ee\u7684\u5728\u65bc\u8ce6\u4e88\u5b57\u8a5e\u4e4b\u9593\u9806\u5e8f\u7684\u95dc\u4fc2\uff0c\u81ea\u7136\u8a9e\u8a00\u8a9e\u610f\u6703 \u6839\u64da\u8a5e\u5f59\u7684\u9806\u5e8f\u800c\u6709\u6240\u4e0d\u540c\uff0c\u82e5\u662f\u4f7f\u7528 BOW \u7684\u65b9\u5f0f\u7de8\u78bc\uff0c\u8a5e\u5f59\u51fa\u73fe\u5728\u53e5\u5b50\u4efb\u610f\u4f4d\u7f6e\u5c0d \u65bc\u7de8\u78bc\u4e26\u6c92\u6709\u4e0d\u540c\uff0c\u4f46\u65bc\u5be6\u969b\u8a9e\u8a00\u76f8\u540c\u8a5e\u5f59\u65bc\u4e0d\u540c\u4f4d\u7f6e\uff0c\u5c0d\u65bc\u8a9e\u610f\u7406\u89e3\u53ef\u80fd\u6703\u6709\u5f88\u5927\u7a0b \u5ea6\u7684\u4e0d\u4e00\u6a23\uff0c\u5982\u4e0b\u65b9\u7bc4\u4f8b\u6240\u793a\uff0c\u76f8\u540c\u7528\u8a5e\u65bc\u4e0d\u540c\u4f4d\u7f6e\u6240\u5f97\u51fa\u7684\u8a9e\u610f\u76f8\u5dee\u751a\u5927\u3002 \u672c\u5be6\u9a57\u4f4d\u7f6e\u7de8\u78bc\u63a1\u7528\u8a13\u7df4\u7684\u65b9\u5f0f\u9054\u6210\uff0c\u900f\u904e mask \u7684\u65b9\u6cd5\u70ba\u8a9e\u53e5\u52a0\u5165\u9806\u5e8f\u95dc\u4fc2\uff0c\u5982 \u4e0b\u65b9\u516c\u5f0f(1)\u6240\u793a\u3002{e 1 ,\u2026,e k }\u70ba\u53e5\u5b50\u4e2d\u6bcf\u500b\u8a5e\u5f59\u7684\u7de8\u78bc\u5411\u91cf\uff0c{ 1 ,\u2026, }\u662f\u9700\u8981\u5b78\u7fd2\u7684 multiplicative mask\uff0c\u70ba\u53ef\u8a13\u7df4\u7684\u5411\u91cf\u3002\u4f7f\u7528\u9019\u500b mask \u7684\u76ee\u7684\u5728\u65bc\u52a0\u5165\u4f4d\u7f6e\u8cc7\u8a0a\u3002\u900f\u904e\u8a13 \u7df4\u7684\u904e\u7a0b\u66f4\u65b0\u6b0a\u91cd\uff0c\u7576\u76f8\u540c\u8a5e\u5f59\u65bc\u4e0d\u540c\u7684\u4f4d\u7f6e\u6642\uff0c\u6240\u4e58\u4e0a\u7684 \u5411\u91cf\u4e5f\u6703\u6709\u6240\u4e0d\u540c\u3002\u900f\u904e \u9019\u6a23\u7684\u65b9\u5f0f\u5c07\u4f4d\u7f6e\u7684\u8a0a\u606f\u52a0\u5165\u81f3\u7de8\u78bc\u4e2d\uff0c\u6700\u5f8c\u5c07\u5176\u52a0\u7e3d\u8868\u793a\u6574\u9ad4\u53e5\u5b50\u7684\u5411\u91cf\u3002 \u2211 \u2a00 (1) 3.3 \u52d5\u614b\u8a18\u61b6\u6a21\u7d44 (Dynamic Memory Module) \u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u7531\u5169\u500b\u90e8\u5206\u7d44\u6210\uff0c\u5206\u5225\u70ba\u8a18\u61b6\u5132\u5b58\u69fd\u8207\u95dc\u4fc2\u5132\u5b58\u69fd\uff0c\u8a18\u61b6\u69fd\u4ee5 key-value \u7684\u5f62\u5f0f\u4fdd\u5b58\uff0ckey \u4fdd\u5b58\u5be6\u9ad4\u3001value \u4fdd\u5b58\u72c0\u614b\uff0c\u66f4\u65b0\u5b8c\u8a18\u61b6\u5f8c\u518d\u4f9d\u64da\u8a18\u61b6\u8207\u8a18\u61b6\u4e4b\u9593\u7684\u95dc \u806f\u66f4\u65b0\u95dc\u4fc2\u69fd\u5167\u5bb9\uff0c\u8a18\u61b6\u69fd\u8207\u95dc\u4fc2\u69fd\u6578\u91cf\u76f8\u7b49\uff0c\u67b6\u69cb\u5982\u4e0b\u65b9\u5716 2 \u6240\u793a\u3002\u672c\u67b6\u69cb\u8207 EntNet \u7684\u5dee\u5225\u70ba\u672c\u7814\u7a76\u52a0\u5165\u4e86\u95dc\u4fc2\u5132\u5b58\u69fd r\u3002\u65b0\u52a0\u5165\u90e8\u4efd\u5728\u5716\u4e2d\u4ee5\u8f03\u7c97\u4e4b\u7dda\u689d\u7e6a\u51fa\u3002 \u5716 2John went to hallway.=>{key:John,value:went to hallway} \u7576\u6bcf\u500b\u53e5\u5b50\u8f38\u5165\u81f3\u6a21\u578b\u5167\u6642\uff0c\u7cfb\u7d71\u900f\u904e\u516c\u5f0f(2)\u8a08\u7b97\u53e5\u5b50\u8207 key\u3001value \u4e4b\u9593\u7684\u95dc\u4fc2\u3002 \u03c3\u8868\u793a sigmoid activation function\uff0cg i \u662f gate\uff0c\u8f38\u51fa\u6578\u503c\u5c07\u4ecb\u65bc 0~1 \u4e4b\u9593\uff0c\u6b64\u6578\u503c\u70ba\u9580\u63a7 \u6a5f\u5236\uff0c\u7528\u4ee5\u6c7a\u5b9a\u66f4\u65b0\u8207\u4fdd\u5b58\u591a\u5c11\u8a18\u61b6\u5167\u5bb9\u3002g i \u7531 w j \u548c h j \u6c7a\u5b9a\u3002\u524d\u8005\u8868\u793a\u8207\u95dc\u9375\u5b57\u7684\u5339\u914d \u7a0b\u5ea6\uff0c\u5f8c\u8005\u8868\u793a\u8207 memory \u5167\u5bb9\u7684\u5339\u914d\u7a0b\u5ea6\u3002\u8207\u6b64\u8a18\u61b6\u69fd\u5be6\u9ad4\u8d8a\u76f8\u95dc\u7684\u8a9e\u53e5\uff0c\u6240\u8a08\u7b97\u51fa \u7684\u6578\u503c\u6703\u8d8a\u9ad8\u3002\u516c\u5f0f(3)\u70ba RNN \u7684\u8a08\u7b97\u516c\u5f0f\uff0c\u7528\u4ee5\u8a08\u7b97\u51fa\u8f38\u5165\u53e5\u5b50\u7684\u5167\u5bb9\u3002 \u8868\u793a\u9700\u8981 \u65b0\u589e\u5230\u5df2\u6709\u7684 memory \u4e2d\u7684\u72c0\u614b\u503c\u3002 \u03d5 \u53ef\u4ee5\u662f\u4efb\u610f\u7684 activation function\uff0c\u5be6\u9a57\u9032\u884c\u6642\u4f7f \u7528\u7684\u662f PReLU\u3002 \u3001 \u3001 \u7686\u70ba\u53ef\u8a13\u7df4\u6b0a\u91cd,\u4e26\u4e14\u6240\u6709\u7684 gated RNN \u5171\u4eab\u9019\u4e9b\u5f15\u6578\uff0c\u65bc\u6574 \u9ad4\u6a21\u578b\u8a13\u7df4\u6642\u4e00\u8d77\u66f4\u65b0\u3002\u516c\u5f0f(4)\u66f4\u65b0\u6bcf\u500b\u8a18\u61b6\u69fd h j \u5167\u5bb9\uff0c\u5c07\u539f\u8a18\u61b6\u69fd\u5167\u5bb9\u9580\u63a7\u8207\u65b0\u8a18\u61b6 \u76f8\u52a0\uff0c\u900f\u904e\u9580\u63a7\u6578\u503c\u63a7\u5236\u66f4\u65b0\u7684\u5e45\u5ea6\u3002\u516c\u5f0f(5)\u7528\u4ee5\u907a\u5fd8\u4e0d\u5fc5\u8981\u8cc7\u8a0a\uff0c\u82e5\u662f\u4e0d\u65b7\u5c07\u65b0\u8a18\u61b6 \u52a0\u5165\u8a18\u61b6\u69fd\u5167\uff0c\u5411\u91cf\u6578\u503c\u5c07\u6703\u8d8a\u4f86\u8d8a\u5927\uff0c\u900f\u904e\u9664\u4e0a normalization \u6578\u503c\uff0c\u4fdd\u6301\u8a18\u61b6\u5411\u91cf\u6578 \u503c\u7bc4\u570d\u3002 \u2190 (2) \u2190 \u2205 (3) \u2190 \u2299 (4) \u2190 (5) \u6a21\u578b\u4e2d\u6bcf\u500b\u95dc\u4fc2\u69fd\u4fdd\u5b58\u5c0d\u61c9\u8a18\u61b6\u8207\u6240\u6709\u5176\u4ed6\u8a18\u61b6\u7684\u95dc\u4fc2\u3002\u66f4\u65b0\u5b8c\u8a18\u61b6\u69fd\u5c07\u6703\u5f97\u5230\u6b64 \u6b21\u8f38\u5165\u5c0d\u65bc\u6bcf\u500b\u8a18\u61b6\u7684\u9580\u63a7\u6578\u503c\uff0c\u5c07\u9580\u63a7\u6578\u503c\u5169\u5169\u76f8\u4e58\u8a08\u7b97\u5f7c\u6b64\u9593\u7684\u95dc\u806f\uff0c\u76f8\u540c\u7684\u9580\u63a7 \u8a08\u7b97\u52a0\u7e3d\u65bc\u76f8\u540c\u95dc\u4fc2\u9580\u63a7\u6578\u503c \uff0c\u5982\u516c\u5f0f(6)\u6240\u793a\u3002\u6b64\u516c\u5f0f\u76ee\u7684\u5728\u65bc\u5c07\u76f8\u540c\u95dc\u806f\u5c0d\u8c61\u4fdd\u5b58 \u5728\u4e00\u8d77\u3002\u5176\u4e2d \uff0c \uff4a \u4f9d\u64da\u76f8\u540c\u95dc\u806f\u5c0d\u8c61\uff0c\u9078\u64c7\u76f8\u95dc\u7684\u8a18\u61b6\u69fd\u3002\u4f8b\u5982\u6a21\u578b\u6709 20 \u500b\u8a18\u61b6\u69fd\uff0c \u8a18\u61b6\u69fd 1 \u8207\u5176\u4ed6\u6240\u6709\u7684\u8a18\u61b6\u69fd\u8a08\u7b97\u51fa 19 \u500b\u95dc\u4fc2\uff0c\u5c07\u9019\u4e9b\u95dc\u4fc2\u4fdd\u5b58\u65bc\u7b2c\u4e00\u500b\u95dc\u4fc2\u69fd\u3002\u5982\u6b64\uff0c \u5f8c\u7e8c\u63a8\u7406\u6642\u53ef\u76f4\u63a5\u5f9e\u5c0d\u61c9\u95dc\u4fc2\u69fd\u627e\u51fa\u8207\u5176\u4ed6\u8a18\u61b6\u7684\u95dc\u806f\uff0c\u5982\u516c\u5f0f(7)\u6240\u793a\u3002 , (6) , (7) \u516c\u5f0f(8)\u8a08\u7b97\u95dc\u4fc2\u66f4\u65b0\u5167\u5bb9\uff0c\u5411\u91cf A\u3001B \u70ba\u53ef\u8a13\u7df4\u6b0a\u91cd\uff0c\u6839\u64da\u539f\u672c\u95dc\u4fc2\u69fd\u5167\u5bb9 \u8207\u8f38 \u5165\u53e5\u5b50 \u6240\u9700\u8981\u66f4\u65b0\u7684\u5167\u5bb9\uff0c\u6700\u5f8c\u4ee5 PReLU \u70ba activation function\u3002\u516c\u5f0f(9)\u70ba\u95dc\u4fc2\u66f4\u65b0\u3002 \u5c07\u95dc\u4fc2\u9580\u63a7\u6578\u503c\u4e58\u4e0a\u65b0\u7684\u95dc\u4fc2\u5167\u5bb9\uff0c\u4e26\u52a0\u4e0a\u539f\u672c\u7684\u95dc\u4fc2\u69fd\u5167\u5bb9\u7528\u4ee5\u66f4\u65b0\u95dc\u4fc2\u69fd\u8cc7\u8a0a\u3002 (8) \u2190 \u2299 \u0303 (9) 3.4 \u8f38\u51fa\u6a21\u7d44 (Output Module) \u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u66f4\u65b0\u5b8c\u8a18\u61b6\u69fd \u8207\u95dc\u4fc2\u69fd \uff0c\u5c07\u6700\u5f8c\u72c0\u614b\u4fdd\u5b58\u7d66\u8f38\u5165\u6a21\u7d44\u63a8\u7406\u4f7f\u7528\u3002\u516c\u5f0f(10) \u5c07\u540c\u500b\u8a18\u61b6\u69fd \u8207\u95dc\u4fc2\u69fd \u5411\u91cf\u4e26\u63a5\u5728\u4e00\u8d77\uff0c\u4e26\u4e58\u4e0a\u53ef\u8a13\u7df4\u6b0a\u91cd \u8a08\u7b97\u51fa\u8a18\u61b6 \u3002\u7136\u5f8c\u518d \u900f\u904e\u6ce8\u610f\u529b\u6a5f\u5236\u8a08\u7b97\u8207 query \u76f8\u95dc\u7684 \u6578\u503c\uff0c\u6578\u503c\u8d8a\u9ad8\u4ee3\u8868\u76f8\u95dc\u6027\u8d8a\u9ad8\uff0c\u5982\u516c\u5f0f(11)\u6240\u793a\u3002 ; (10) (11) \u5c07\u6ce8\u610f\u529b\u6578\u503c\u4e58\u4e0a\u5c0d\u61c9\u8a18\u61b6\uff0c\u8d8a\u76f8\u95dc\u8a18\u61b6\u6578\u503c\u76f8\u5c0d\u6703\u8d8a\u9ad8\u5982\u516c\u5f0f(12)\u6240\u793a\uff0c\u4ee5\u4fdd\u7559 \u8207\u554f\u984c\u76f8\u95dc\u91cd\u8981\u8cc7\u8a0a\u3002\u7cfb\u7d71\u6700\u5f8c\u6839\u64da\u516c\u5f0f(13)\u63a8\u7406\u51fa\u6700\u5f8c\u554f\u984c\u7684\u7b54\u6848\uff0c\u5176\u4e2d R \u8ddf H \u70ba\u53c3 \u6578\u77e9\u9663\u3002query \u554f\u984c\u6703\u4f9d\u7167\u8a13\u7df4\u6642\u7684\u65b9\u5f0f\u88ab\u7de8\u78bc\u6210 k \u500b\u7dad\u5ea6\u7684\u5411\u91cf q\u3002\u672c\u7814\u7a76\u4f7f\u7528\u6578\u64da\u70ba \u554f\u7b54\u4efb\u52d9\uff0c\u7cfb\u7d71\u6839\u64da\u8a5e\u5f59\u5eab\u8f38\u51fa\u6700\u6709\u53ef\u80fd\u7684\u7b54\u6848 y\u3002 (12) \u00d8 (13) \u5f9e\u7b2c\u4e8c\u7ae0\u6587\u737b\u63a2\u8a0e\u53ef\u767c\u73fe\uff0c\u591a\u8df3\u8e8d\u6a5f\u5236\u6709\u52a9\u65bc\u63d0\u5347\u6a21\u578b\u63a8\u7406\u80fd\u529b\uff0c\u672c\u7814\u7a76\u5617\u8a66\u5c07\u6b64 \u6982\u5ff5\u52a0\u5165\u63a8\u7406\u6a21\u7d44\uff0c\u5c07\u4e0a\u65b9\u8a18\u61b6\u8207\u6ce8\u610f\u529b\u6b0a\u91cd\u76f8\u4e58\u52a0\u7e3d\u7684\u5411\u91cf u\uff0c\u8207\u539f query \u5411\u91cf\u76f8\u52a0\uff0c \u4f5c\u70ba\u65b0\u7684 query \u5411\u91cf\uff0c\u91cd\u8907\u516c\u5f0f(11)(12)\u8a08\u7b97\uff0c\u6bcf\u591a\u4e00\u6b21\u8a08\u7b97 hop \u6578\u589e\u52a0 1\uff0c\u539f\u672c\u63a8\u7406\u6a21 \u7d44\u70ba hop1\uff0c\u91cd\u8907\u4e00\u6b21\u8a08\u7b97\u70ba hop2\uff0c\u4f9d\u6b64\u985e\u63a8\uff0c\u5982\u516c\u5f0f(14)\u6240\u793a\u3002 (14) 3.5 \u8a0e\u8ad6 (Discussion) \u672c\u7814\u7a76\u4ee5 EntNet \u6a21\u578b\u70ba\u7814\u7a76\u57fa\u790e\uff0c\u5617\u8a66\u900f\u904e\u7d50\u5408\u8a18\u61b6\u95dc\u806f\u8a08\u7b97\uff0c\u589e\u52a0\u8a18\u61b6\u9593\u7684\u806f\u7e6b\uff0c\u800c \u975e\u4e0d\u540c\u8a18\u61b6\u69fd\u7368\u7acb\u904b\u4f5c\u3002\u5728 3.1 \u7bc0\u4e2d\u4ecb\u7d39\u6574\u9ad4\u6a21\u578b\u67b6\u69cb\u3002\u4e3b\u8981 Encode \u6a21\u7d44\u3001\u52d5\u614b\u8a18\u61b6\u6a21 \u7d44\u4ee5\u53ca\u8f38\u51fa\u6240\u7d44\u6210\u30023.2 \u7bc0\u4ecb\u7d39\u6587\u5b57\u5982\u4f55\u8f49\u63db\u70ba\u5411\u91cf\u5f62\u5f0f\u8868\u793a\u3002\u5f9e\u5efa\u7acb\u57fa\u672c\u8a5e\u5f59\u5eab\u5230\u8a13\u7df4 \u8a5e\u5f59\u5411\u91cf\u7684\u904e\u7a0b\u30023.3 \u7bc0\u4e2d\u4ecb\u7d39\u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u7d30\u7bc0\u3002\u8ca0\u8cac\u8a18\u61b6\u4fdd\u5b58\u8207\u66f4\u65b0\u7684\u90e8\u5206\uff0c\u9664\u4e86\u539f \u8a18\u61b6\u6a21\u578b\u7684\u8a18\u61b6\u69fd\u5916\uff0c\u5c07\u95dc\u4fc2\u7684\u8a08\u7b97\u52a0\u5165\u6a21\u578b\u5167\uff0c\u4f7f\u4e0d\u540c\u7684\u8a18\u61b6\u69fd\u53ef\u900f\u904e\u95dc\u806f\u8a08\u7b97\uff0c\u8a08 \u7b97\u5f7c\u6b64\u7684\u95dc\u4fc2\uff0c\u8a18\u61b6\u9593\u7684\u95dc\u806f\u8a08\u7b97\u96a8\u8457\u8a18\u61b6\u69fd\u6578\u91cf\u800c\u5feb\u901f\u589e\u9577\uff0c\u5c07\u5176\u63d0\u53d6\u70ba\u540c\u6a23\u8a18\u61b6\u6578 \u91cf\u7684\u95dc\u806f\u69fd\uff0c\u53ef\u964d\u4f4e\u6b0a\u91cd\u8207\u8a08\u7b97\u91cf\u30023.4 \u7bc0\u70ba\u8f38\u51fa\u6a21\u7d44\u7684\u7d30\u7bc0\u3002\u7576\u8a18\u61b6\u6a21\u7d44\u5c07\u5148\u9a57\u77e5\u8b58\u4fdd \u5b58\u5f8c\uff0c\u8f38\u51fa\u6a21\u7d44\u91dd\u5c0d\u554f\u984c\u5f9e\u8a18\u61b6\u4e2d\u53d6\u51fa\u76f8\u95dc\u7684\u90e8\u5206\uff0c\u4e26\u63a8\u7406\u51fa\u6700\u5f8c\u7684\u7b54\u6848\u3002\u7814\u7a76\u65b9\u6cd5\u4e2d \u7684\u95dc\u4fc2\u8a08\u7b97\u8207\u591a\u8df3\u8e8d\u7684\u63a8\u7406\u65b9\u6cd5\uff0c\u53ef\u6cdb\u5316\u61c9\u7528\u5230\u4e0d\u540c\u7684\u8a18\u61b6\u7db2\u8def\u67b6\u69cb\uff0c\u6216\u662f\u5177\u6709\u8a18\u61b6\u4fdd \u5b58\u7684\u67b6\u69cb\u7684\u6a21\u578b\u4e0a\u3002 4. \u5be6\u9a57 (Experiments) \u672c\u7814\u7a76\u6240\u6709\u5be6\u9a57\u9078\u64c7\u4ee5 bAbI dataset \u505a\u70ba\u5be6\u9a57\u9a57\u8b49\u7684\u6578\u64da\u96c6\uff0c\u6b64\u6578\u64da\u96c6\u70ba\u81ea Facebook AI Research (FAIR)\u6240\u63d0\u4f9b\u7684\u7d9c\u5408\u95b1\u8b80\u7406\u89e3\u548c\u554f\u7b54\u8cc7\u6599\u96c6\u3002\u9078\u64c7\u6b64\u6578\u64da\u96c6\u9a57\u8b49\u76ee\u7684\u6709\u56db\u9ede\uff0c \u5206\u5225\u5982\u4e0b\uff1a (\u4e00) \u6578\u64da\u96c6\u5305\u542b\u4e86\u4e8c\u5341\u7a2e\u4efb\u52d9\uff0c\u53ef\u5f9e\u4e0d\u540c\u9762\u5411\u6e2c\u8a66\u6a21\u578b\u7684\u512a\u52e2\u8207\u52a3\u52e2\u3002 (\u4e8c) \u70ba\u554f\u7b54\u8207\u81ea\u7136\u8a9e\u8a00\u7406\u89e3\u5e38\u7528\u6578\u64da\u96c6\uff0c\u6709\u8a31\u591a\u4e0d\u540c\u6a21\u578b\u5be6\u9a57\u6578\u64da\u53ef\u53c3\u8003\u6bd4\u8f03\u3002 (\u4e09) \u5305\u542b\u82f1\u6587\u3001\u5370\u5730\u8a9e\u8207\u6539\u7d44(\u4eba\u985e\u4e0d\u53ef\u95b1\u8b80) \u7b49\u6578\u64da\uff0c\u53ef\u4e86\u89e3\u8a9e\u8a00\u6a21\u578b\u61c9\u7528\u65bc\u4e0d\u540c\u81ea\u7136\u8a9e \u8a00\u4e4b\u6548\u679c\u3002 (\u56db) \u65bc 20 \u9805\u4efb\u52d9\u4e2d\u63d0\u4f9b 1k \u8cc7\u6599\u91cf\u8207 10k \u8cc7\u6599\u91cf\uff0c\u53ef\u5be6\u9a57\u6578\u64da\u91cf\u591a\u5be1\u5c0d\u65bc\u6a21\u578b\u5b78\u7fd2\u7684\u5f71\u97ff\u3002 \u672c\u7814\u7a76\u76ee\u6a19\u70ba\u65bc\u6578\u64da\u96c6 1k \u7684\u524d\u63d0\u4e0b\uff0c\u63d0\u5347\u6a21\u578b\u8a13\u7df4\u6548\u679c\u3002 \u4ee5\u4e0b\u5be6\u9a57\u70ba\u6c42\u6e96\u78ba\u6027\uff0c\u4ee5\u4ea4\u53c9\u9a57\u8b49\u65b9\u5f0f\uff0c\u900f\u904e\u4e0d\u540c\u8a13\u7df4\u6578\u64da\u505a\u9a57\u8b49\uff0c\u4e26\u5e73\u5747\u591a\u6b21\u8a13 \u9a57\u8b49\u6539\u5584\u6548\u679c\u3002 \u4fdd\u5b58\u65bc\u7b2c\u4e00\u500b\u95dc\u4fc2\u69fd\uff0c\u95dc\u4fc2\u69fd\u6578\u91cf\u8207\u8a18\u61b6\u69fd\u6578\u91cf\u76f8\u7b49\uff0c\u78ba\u4fdd\u95dc\u4fc2\u4fdd\u5b58\u4e0d\u6703\u96a8\u8a18\u61b6\u69fd\u589e\u9577 \u800c\u5927\u91cf\u589e\u52a0\u3002\u95dc\u806f\u8a08\u7b97\u900f\u904e Cm 2 \u6392\u5217\u7d44\u5408\u8a08\u7b97\uff0c\u5982\u516c\u5f0f(15)\u6240\u793a\u300220 \u500b\u8a18\u61b6\u69fd\u8a08\u7b97\u51fa Task 14: Time Reasoning 111700 \u65b9\u5f0f\u6539\u5584\u3002\u8fd1\u5e7e\u5e74\u7684\u8a9e\u8a00\u6a21\u578b\u7814\u7a76\u591a\u70ba\u9810\u8a13\u7df4(Pre-train)\u8207\u5fae\u8abf(Fine-tuning)\u7684\u65b9\u6cd5\uff0c\u900f\u904e 81700 \u5be6\u9a57\u4e8c\u65bc\u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u4e2d\u505a\u6539\u5584\u3002\u900f\u904e\u8a18\u61b6\u9593\u7684\u95dc\u806f\uff0c\u8a08\u7b97\u9023\u7d50\u6b65\u52d5\u8a18\u61b6\u9593\u7684\u95dc\u4fc2\u3002 \u672c\u8ad6\u6587\u6240\u6539\u5584\u7684\u90e8\u5206\u7686\u843d\u65bc\u8a18\u61b6\u4fdd\u5b58\u8207\u63a8\u7406\u7684\u90e8\u5206\uff0c\u7de8\u78bc\u7684\u90e8\u5206\u61c9\u80fd\u900f\u904e\u9810\u8a13\u7df4\u7684 \u7df4\u7d50\u679c\u3002\u5be6\u9a57\u4f7f\u7528 1k \u6578\u64da\u91cf\u8a13\u7df4\u6a21\u578b\uff0c\u4e26\u5c07 10k \u6578\u64da\u5207\u5206\u591a\u4efd 1k \u6a94\u6848\uff0c\u900f\u904e\u591a\u6b21\u5be6\u9a57 4.1 \u5be6\u9a57\u4e00(\u591a\u9ede\u8df3\u8e8d\u8a0a\u606f\u63a8\u7406) \u5be6\u9a57\u76ee\u7684\uff1a \u7531\u524d\u9762\u7684\u5be6\u9a57\u53ca\u8a0e\u8ad6\u4e2d\uff0c\u6211\u5011\u53ef\u4ee5\u770b\u51fa\u591a\u8df3\u8e8d\u91dd\u5c0d\u8907\u96dc\u7684\u554f\u7b54\u63a8\u7406\uff0c\u666e\u904d\u76f8\u8f03\u65bc\u55ae\u8df3\u8e8d \u5c0d\u65bc\u63a8\u7406\u7d50\u679c\u6548\u679c\u66f4\u597d\uff0c\u800c EntNet \u6a21\u578b\u5c6c\u65bc\u55ae\u8df3\u8e8d\u6a21\u578b\uff0c\u672c\u5be6\u9a57\u5617\u8a66\u5c07\u591a\u8df3\u8e8d\u7684\u6982\u5ff5\u61c9 \u7528\u65bc\u8f38\u51fa\u6a21\u7d44\u4e2d\uff0c\u5617\u8a66\u589e\u5f37\u905e\u6b78\u795e\u7d93\u7db2\u8def\u63a8\u7406\u80fd\u529b\u3002 \u5be6\u9a57\u5167\u5bb9\uff1a \u672c\u5be6\u9a57\u5c07\u5f15\u5165\u7aef\u5c0d\u7aef\u8a18\u61b6\u7db2\u8def\u7684\u591a\u8df3\u8e8d\u63a8\u7406\u516c\u5f0f\uff0c\u9078\u7528\u6b64\u65b9\u6cd5\u539f\u56e0\u5728\u65bc\u7aef\u5c0d\u7aef\u7db2\u8def\u8207 EntNet \u6a21\u578b\u76f8\u4f3c\uff0c\u90fd\u5177\u5099\u8a18\u61b6\u55ae\u5143\u4fdd\u5b58\u8cc7\u8a0a\uff0c\u5176\u4ed6\u6a21\u578b\u67b6\u69cb\u65b9\u6cd5\u5dee\u7570\u8f03\u5927\u3002\u8f38\u51fa\u6a21\u7d44\u8ca0 \u8cac\u63a8\u7406\u7b54\u6848\uff0c\u900f\u904e\u6ce8\u610f\u529b\u6a5f\u5236\u627e\u5c0b\u8207\u6b64\u6b21\u554f\u984c\u76f8\u95dc\u7684\u8a18\u61b6\uff0c\u4f9d\u64da\u8a18\u61b6\u5167\u5bb9\u63a8\u7406\u51fa\u6700\u7d42\u7b54 \u6848\uff0c\u800c\u7d93\u904e\u4e00\u6b21\u6ce8\u610f\u6a5f\u5236\u7684\u8a08\u7b97\u70ba\u55ae\u8df3\u8e8d\uff0c\u672c\u5be6\u9a57\u5617\u8a66\u589e\u52a0\u8df3\u8e8d\u6578\u91cf\u3002\u5982 3.4 \u5c0f\u7bc0\u4e2d\u7684 \u516c\u5f0f(14)\uff0c\u6ce8\u610f\u529b\u6a5f\u5236\u8a08\u7b97\u51fa\u7684\u6578\u503c\u8207\u8a08\u7b97\u6240\u4f7f\u7528\u7684 query \u76f8\u52a0\uff0c\u505a\u70ba\u65b0\u7684 query \u518d\u6b21\u8207 \u8a18\u61b6\u505a\u6ce8\u610f\u529b\u6a5f\u5236\u7684\u8a08\u7b97\uff0c\u6bcf\u591a\u505a\u4e00\u6b21\u8df3\u8e8d\u6578\u589e\u52a0 1\uff0c\u5be6\u9a57\u5c07\u6bd4\u8f03\u96d9\u8df3\u8e8d\u8207\u539f\u5148\u55ae\u8df3\u8e8d \u7684\u5dee\u7570\u3002\u5be6\u9a57\u7d50\u679c\u6574\u7406\u65bc\u8868 6 \u5167\u4e4b Multi hop \u6b04\u4f4d\u3002 \u7531\u5be6\u9a57\u6578\u64da\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u589e\u52a0\u8df3\u8e8d\u6578\u91cf\u4e26\u6c92\u6709\u589e\u52a0\u6a21\u578b\u7684\u6e96\u78ba\u7387\uff0c\u751a\u81f3\u90e8\u5206\u7684\u6e96\u78ba \u7387\u76f8\u8f03\u65bc\u539f\u6a21\u578b\u6709\u4e0b\u964d\u7684\u8da8\u52e2\uff0c\u5e73\u5747\u932f\u8aa4\u7387\u53cd\u800c\u4e0a\u5347\u3002\u5206\u6790\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u65bc\u8a13\u7df4\u8cc7\u6599\u3001 \u6e2c\u8a66\u8cc7\u6599\u7684\u8868\u73fe\uff0c\u53ef\u4ee5\u770b\u51fa\u6709\u904e\u64ec\u5408\u7684\u8da8\u52e2\uff0c\u8907\u96dc\u5316\u63a8\u7406\u6a21\u7d44\u7121\u6cd5\u63d0\u5347\u6548\u679c\u3002\u63a8\u6e2c\u70ba\u8a18 \u61b6\u6a21\u7d44\u6240\u4fdd\u5b58\u7684\u8cc7\u8a0a\u4e0d\u8db3\uff0c\u7121\u6cd5\u63d0\u4f9b\u8db3\u5920\u7684\u8cc7\u8a0a\u7d66\u8207\u63a8\u7406\u6a21\u7d44\u9032\u884c\u5f8c\u7e8c\u7684\u63a8\u7406\u3002\u56e0\u6b64\u8a2d \u8a08\u5be6\u9a57\u4e8c\u900f\u904e\u8a18\u61b6\u95dc\u806f\u7684\u8a08\u7b97\uff0c\u8207\u95dc\u4fc2\u69fd\u7684\u4fdd\u5b58\u63d0\u5347\u6a21\u578b\u5916\u90e8\u8a18\u61b6\u4fdd\u5b58\u7684\u80fd\u529b\uff0c\u5617\u8a66\u4fdd \u5b58\u66f4\u95dc\u7684\u8cc7\u8a0a\u662f\u5426\u80fd\u63d0\u5347\u6a21\u578b\u7684\u6548\u679c\u3002 4.2 \u5be6\u9a57\u4e8c(\u8a18\u61b6\u95dc\u806f) \u5be6\u9a57\u76ee\u7684\uff1a \u6b64\u5be6\u9a57\u4e3b\u8981\u61c9\u7528\u65bc EntNet \u6a21\u578b\u7684\u52d5\u614b\u8a18\u61b6\u6a21\u7d44\uff0c\u6839\u64da\u5be6\u9a57\u4e00\u7684\u5be6\u9a57\u7d50\u679c\uff0c\u53ef\u767c\u73fe\u8907\u96dc\u5316 \u63a8\u7406\u6a21\u7d44\u7121\u6cd5\u63d0\u5347\u63a8\u7406\u80fd\u529b\uff0c\u56e0\u6b64\u5be6\u9a57\u4e8c\u5c07\u95dc\u806f\u8a08\u7b97\u52a0\u5165\u8a18\u61b6\u4e4b\u9593\u3002\u76f8\u8f03\u65bc\u539f\u672c\u8a18\u61b6\u5206 \u5225\u7368\u7acb\u4fdd\u5b58\u8a0a\u606f\uff0c\u8a18\u61b6\u95dc\u806f\u7684\u8a08\u7b97\u80fd\u5c07\u76f8\u95dc\u8a18\u61b6\u4e32\u9023\u8d77\u4f86\u3002\u5982\u540c\u4eba\u985e\u8a18\u61b6\u4fdd\u5b58\u4e26\u975e\u628a\u6240 \u6709\u90e8\u5206\u5b8c\u5168\u7368\u7acb\uff0c\u900f\u904e\u806f\u60f3\u53ef\u806f\u7e6b\u5230\u4e0d\u540c\u7684\u60f3\u6cd5\u6216\u8a18\u61b6\u3002\u672c\u5be6\u9a57\u984d\u5916\u4fdd\u5b58\u8a18\u61b6\u8207\u8a18\u61b6\u7684 \u9593\u7684\u95dc\u806f\uff0c\u7528\u4ee5\u589e\u52a0\u63a8\u7406\u6a21\u7d44\u53ef\u7528\u8a0a\u606f\uff0c\u589e\u52a0\u6a21\u578b\u63a8\u7406\u80fd\u529b\u3002 \u5be6\u9a57\u5167\u5bb9\uff1a \u52a0\u5165\u95dc\u4fc2\u69fd\u7528\u4ee5\u4fdd\u5b58\u5c0d\u61c9\u8a18\u61b6\u69fd\u7684\u95dc\u4fc2\uff0c\u5982\u7b2c\u4e00\u500b\u8a18\u61b6\u69fd\u8207\u5176\u4ed6\u6240\u6709\u8a18\u61b6\u69fd\u95dc\u806f\u8a08\u7b97\uff0c Task 13: Compound Coreference 111600 81600 \u91cd\u3002 \u7684\u63a8\u7406\u904e\u7a0b\u3002 \u6a21\u578b\u8a18\u61b6\u4fdd\u5b58\u8207\u63a8\u7406\u80fd\u529b\u3002 \u69fd\u66f4\u65b0\u591a\u5be1\u3002\u5be6\u9a57\u8a18\u61b6\u69fd\u6578\u91cf\u8207\u539f\u6a21\u578b\u76f8\u540c\uff0c\u4f7f\u7528 20 \u500b\u8a18\u61b6\u69fd\u4fdd\u5b58\u91cd\u8981\u8cc7\u8a0a\u3002\u53e6\u5916\u984d\u5916 Task 12: Conjunction 110400 80400 \u8a18\u61b6\u95dc\u806f\u66f4\u65b0\u53ef\u4ee5\u4e0b\u964d\u4e0d\u5c11\u6b0a\u91cd\u904b\u7b97\uff0c\u55ae\u500b\u4efb\u52d9\u53ef\u4e0b\u964d 1 \u842c\u6b0a\u91cd\u91cf\uff0c\u6240\u6709\u4efb\u52d9\u70ba 20 \u842c\u6b0a \u800c\u5be6\u9a57\u4e00\u7684\u5be6\u9a57\u7d50\u679c\u5e73\u5747\u932f\u8aa4\u7387\u53cd\u800c\u7565\u70ba\u63d0\u5347\uff0c\u63a8\u8ad6\u70ba\u8a18\u61b6\u4fdd\u5b58\u7684\u5167\u5bb9\u4e0d\u8db3\u4ee5\u652f\u6490\u8907\u96dc \u4e92\u63a8\u7406\u3002\u672a\u4f86\u82e5\u662f\u95dc\u806f\u8a08\u7b97\u80fd\u5e36\u5165\u7fa4\u7d44\u95dc\u806f\u8a08\u7b97\uff0c\u589e\u5f37\u8a18\u61b6\u9593\u7684\u9023\u7d50\u6027\uff0c\u61c9\u80fd\u518d\u6b21\u63d0\u5347 \u672c\u5be6\u9a57\u5c07\u8a18\u61b6\u69fd\u5169\u5169\u6839\u64da\u516c\u5f0f(6)\u8a08\u7b97\u51fa\u95dc\u4fc2\u9580\u63a7\u6578\u503c\uff0c\u7528\u4ee5\u6c7a\u5b9a\u6b64\u6b21\u6578\u64da\u8f38\u5165\u5c0d\u65bc\u95dc\u4fc2 190 \u500b\u95dc\u4fc2\uff0c\u518d\u5c07 190 \u500b\u95dc\u4fc2\u5206\u5225\u4fdd\u5b58\u5230\u5c0d\u61c9\u7684\u95dc\u4fc2\u69fd\u5167\u3002\u5be6\u9a57\u7d50\u679c\u5982\u8868 6 \u4e2d\u4e4b Relation slot \u6b04\u4f4d\u6240\u793a\u3002 (15) bAbI \u6578\u64da\u96c6\u65bc\u4e0d\u540c\u4efb\u52d9\u6709\u4e0d\u540c\u7684\u63a8\u7406\u96e3\u5ea6\uff0c\u6709\u4e9b\u4efb\u52d9\u9700\u8981\u7d50\u5408\u591a\u9805\u5148\u9a57\u77e5\u8b58\u4ea4\u53c9\u63a8 \u7406\u624d\u80fd\u5f97\u51fa\u7b54\u6848\u3002\u5f9e\u5be6\u9a57\u6578\u64da\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u900f\u904e\u95dc\u806f\u8a08\u7b97\u80fd\u6709\u6548\u964d\u4f4e\u5e73\u5747\u932f\u8aa4\u3002\u76f8\u8f03\u65bc \u539f\u5148\u8a18\u61b6\u7368\u7acb\u4fdd\u5b58\uff0c\u6b64\u65b9\u6cd5\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u5f9e\u6578\u64da\u4e2d\u5b78\u7fd2\u8a5e\u53e5\u9593\u7684\u95dc\u806f\u3002\u4f46\u4e5f\u4e26\u975e\u6240\u6709\u4efb \u52d9\u90fd\u6709\u660e\u986f\u6539\u5584\u3002\u4efb\u52d9 2 \u6539\u5584\u6548\u679c\u6700\u70ba\u660e\u986f\uff0c\u6578\u64da\u7279\u6027\u662f\u627e\u5230\u5169\u9805\u652f\u6301\u4e8b\u5be6\u7684\u53e5\u5b50\uff0c\u624d \u80fd\u63a8\u7406\u51fa\u554f\u984c\u7684\u7b54\u6848\uff0c\u800c\u95dc\u806f\u7684\u8a08\u7b97\u525b\u597d\u662f\u5169\u5169\u8a18\u61b6\u69fd\u7684\u8a08\u7b97\uff0c\u6548\u679c\u986f\u8457\u65bc\u63d0\u5347\u4efb\u52d9 2\u3002 \u4f46\u4efb\u52d9 3 \u66f4\u591a\u7684\u652f\u6301\u4e8b\u5be6\u53e5\u5b50\u537b\u7121\u6cd5\u63d0\u5347\u3002\u63a8\u8ad6\u70ba\u6578\u64da\u96c6\u592a\u5c0f\u4ee5\u53ca\u95dc\u806f\u8a08\u7b97\u7684\u65b9\u6cd5\u7684\u5f71 \u97ff\u3002 \u53e6\u65bc\u8868 5 \u6bd4\u8f03\u4fdd\u5b58\u6240\u6709\u95dc\u806f\u8a08\u7b97(\u5982 RelNet \u6a21\u578b\u5373\u662f\u63a1\u7528\u6b64\u65b9\u6cd5)\uff0c\u8207\u95dc\u806f\u63d0\u53d6\u5169\u7a2e \u4e2d 20 \u500b\u8a18\u61b6\u69fd\u5c07\u6703\u8a08\u7b97\u51fa 190 \u500b\u95dc\u4fc2\uff0c\u6b64\u5be6\u9a57\u5c07 190 \u500b\u95dc\u4fc2\u6578\u503c\u63d0\u53d6\u65bc 20 \u500b\u95dc\u4fc2\u69fd\u4fdd \u95dc\u806f\u7684\u6b0a\u91cd\u6578\u91cf\u964d\u4f4e\u4e86 26.8%\u3002\u5be6\u9a57\u7d50\u679c\u7684\u6578\u64da\u53ef\u4ee5\u767c\u73fe\u5728\u4e0d\u540c\u4efb\u52d9\u63d0\u5347\u6548\u679c\u4e0d\u540c\uff0c\u4e5f \u6709\u90e8\u5206\u6e96\u78ba\u7387\u662f\u4e9b\u5fae\u4e0b\u964d\uff0c\u4f46\u6574\u9ad4\u4ecd\u4ee5\u63d0\u5347\u70ba\u4e3b\u3002 \u8868 5Task All relation method Relation slot Task 1: Single Supporting Fact 110000 80000 Task 2: Two Supporting Facts 112900 Task 3: Three Supporting Facts 113400 Task 4: Two Argument Relations 109400 79400 Task 5: Three Argument Relations 115200 85200 Task 6: Yes/No Questions 113400 83400 Task 7: Counting 115100 85100 Task 8: Lists/Sets 115100 85100 Task 9: Simple Negation 111100 Task 10: Indefinite Knowledge 111500 Task 11: Basic Coreference 111600 81600 \u2190 \u2299 \u0303 (19) \u8a08\u7b97\u96d6\u80fd\u63d0\u5347\u6a21\u578b\u63a8\u7406\u6548\u679c\uff0c\u4f46\u82e5\u662f\u76f4\u63a5\u66f4\u65b0\u8a18\u61b6\u69fd\u672c\u8eab\uff0c\u53cd\u800c\u6703\u9020\u6210\u8a18\u61b6\u4fdd\u5b58\u7684\u6548\u679c \u4e0b\u964d\uff0c\u76ee\u524d\u4ecd\u662f\u5c07\u5169\u8005\u5206\u958b\u4fdd\u5b58\u6548\u679c\u8f03\u597d\u3002\u4f46\u6839\u64da\u8868 7 \u6b0a\u91cd\u7684\u6578\u91cf\u6bd4\u8f03\uff0c\u53ef\u4ee5\u767c\u73fe\u81ea\u6211 \u4e00\u65bc\u8f38\u51fa\u6a21\u7d44\u4e2d\u505a\u66f4\u52d5\uff0c\u900f\u904e\u91cd\u8907\u6027\u7684\u6ce8\u610f\u529b\u6a5f\u5236\u8a08\u7b97\uff0c\u5617\u8a66\u63d0\u5347\u8907\u96dc\u4efb\u52d9\u7684\u63a8\u7406\u80fd\u529b\u3002 \u7403\u3001\u7fbd\u6bdb\u7403\u8207\u8db3\u7403\u4e09\u8005\u7686\u5c6c\u65bc\u7403\u985e\u3002bAbI \u6578\u64da\u96c6\u4e2d\u7684\u4efb\u52d9 3 \u4e5f\u9700\u8981\u9700\u8981\u66f4\u591a\u5148\u9a57\u77e5\u8b58\u4ea4 \u524d\u9762\u7684\u4e09\u500b\u5be6\u9a57\u4e2d\u4e3b\u8981\u63a2\u8a0e\u5169\u500b\u65b9\u5411\uff1a\u6e96\u78ba\u7387\u8207\u6b0a\u91cd\u8a08\u7b97\u91cf\u3002\u5f9e\u6e96\u78ba\u7387\u65b9\u9762\u4f86\u770b\uff0c\u5be6\u9a57 \u7b97\uff0c\u4f46\u73fe\u5be6\u4e16\u754c\u4e0d\u540c\u7684\u5be6\u9ad4\u95dc\u4fc2\u53ef\u4ee5\u662f\u5169\u500b\u3001\u4e09\u500b\u6216\u662f\u7fa4\u9ad4\u9593\u5177\u6709\u4e00\u5b9a\u7684\u95dc\u806f\uff0c\u4f8b\u5982\u7c43 81500 \u2190 (18) \u76f8\u8f03\u65bc\u5be6\u9a57\u4e8c\u52a0\u5165\u95dc\u806f\u8a08\u7b97\u7684\u63d0\u5347\uff0c\u6b64\u5be6\u9a57\u5e73\u5747\u6e96\u78ba\u7387\u53cd\u800c\u4e0b\u964d\u4e0d\u5c11\u3002\u63a8\u8ad6\u70ba\u95dc\u806f 4.4 \u5be6\u9a57\u7e3d\u7d50 (Experiment summary) \u95dc\u806f\u8a08\u7b97\u6216\u8a31\u53ef\u63d0\u5347\u5176\u4ed6\u4efb\u52d9\u7684\u6e96\u78ba\u7387\u3002\u672c\u7814\u7a76\u7684\u95dc\u4fc2\u8a08\u7b97\u6240\u63a1\u7528\u7684\u662f\u5169\u5169\u8a18\u61b6\u69fd\u7684\u8a08 81100 \u793a\uff0c\u8a08\u7b97\u6b64\u6b21\u8f38\u5165\u53e5\u5b50\u5728\u5169\u5169\u8a18\u61b6\u69fd\u9593\u7684\u95dc\u4fc2\uff0c\u65b9\u6cd5\u8207\u5be6\u9a57\u4e8c\u76f8\u4f3c\uff0c\u800c\u516c\u5f0f(19)\u900f\u904e\u9580 \u63a7\u6578\u503c\u6c7a\u5b9a\u6b64\u6b21\u66f4\u65b0\u7684\u591a\u5be1\uff0c\u800c\u66f4\u65b0\u7684\u76ee\u6a19\u662f\u8a18\u61b6\u672c\u8eab\u3002\u5be6\u9a57\u7d50\u679c\u5982\u8868 6 \u4e2d\u4e4b Self memory \u6b04\u4f4d\u6240\u793a\u3002 , (16) , , (17) Task 17: Positional Reasoning 41.20% 39.00% 37.70% 39.40% Task 18: Size Reasoning 8.00% 7.60% 6.20% 8.50% Task 19: Path Finding 87.80% 86.80% 85.30% 87.60% Task 20: Agent's Motivations 0.90% 0.20% 0.90% 16.24% 17.74% 14.96% 26.00% 11 11 9 15 \u52d9\u63d0\u5347\u7684\u6548\u679c\u4e0d\u540c\uff0c\u5176\u4e2d\u4efb\u52d9 2 \u63d0\u5347\u6548\u679c\u6700\u70ba\u660e\u986f\u3002\u8207\u4efb\u52d9\u672c\u8eab\u7684\u7279\u6027\u6709\u95dc\uff0c\u900f\u904e\u66f4\u591a Failed Tasks(error>5%) Mean parameter 82010 72010 \u4ee5\u672c\u8ad6\u6587\u70ba\u57fa\u790e\uff0c\u672a\u4f86\u9084\u53ef\u671d\u95dc\u4fc2\u8a08\u7b97\u65b9\u6cd5\u9032\u884c\u6539\u5584\u3002\u5f9e\u5be6\u9a57\u4e8c\u4e2d\u53ef\u4ee5\u770b\u51fa\u4e0d\u540c\u4efb Mean Error Sum of all task parameter 1640200 1440200 \u4ee5\u7528\u540c\u6a23\u7684\u6982\u5ff5\u8a08\u7b97\u7269\u9ad4\u3001\u6578\u64da\u3001\u8a5e\u53e5\u4ee5\u53ca\u6642\u9593\u4e0a\u7684\u95dc\u4fc2\u3002 2.90% Task 18: Size Reasoning 80700 Task 19: Path Finding 83200 Task 20: Agent's Motivations 83600 \u5be6\u9a57\u6240\u4f7f\u7528\u7684 EntNet \u6a21\u578b\uff0c\u4e5f\u53ef\u4ee5\u904b\u7528\u65bc\u4e0d\u540c\u8a18\u61b6\u7db2\u8def\u67b6\u69cb\u5167\u3002\u800c\u975e\u8a18\u61b6\u7db2\u8def\u6a21\u578b\u4e5f\u53ef 73600 \u900f\u904e\u5be6\u9a57\u4e8c\u5be6\u9a57\u7d50\u679c\u53ef\u4ee5\u770b\u51fa\u6574\u9ad4\u6e96\u78ba\u7387\u53ef\u4ee5\u6709\u6548\u7684\u63d0\u5347\uff0c\u800c\u9019\u6a23\u7684\u65b9\u6cd5\u4e0d\u4fb7\u9650\u65bc 73200 \u7684\u7684\u8a08\u7b97\u91cf\u3002 70700 \u8cc7\u8a0a\u65bc\u95dc\u4fc2\u69fd\u5167\uff0c\u53ef\u4ee5\u5927\u91cf\u6e1b\u5c11\u8a18\u61b6\u5132\u5b58\u6240\u9700\u8981\u5360\u7528\u7684\u5927\u5c0f\uff0c\u4ee5\u53ca\u8f38\u51fa\u6a21\u7d44\u6240\u9700\u8981\u63a8\u7406 83400 \u8f38\u5165\u95dc\u806f\u69fd\u7684\u90e8\u5206\u6539\u6210\u8a18\u61b6\u69fd\u672c\u8eab\uff0c\u8f38\u51fa\u6240\u66f4\u65b0\u7684\u76ee\u6a19\u4e5f\u662f\u8a18\u61b6\u69fd\u3002\u5982\u516c\u5f0f(16)~(18)\u6240 Task 16: Basic Induction 50.00% 50.70% 51.00% 51.20% Task 17: Positional Reasoning 81100 71100 \u7528\u7684\u8a18\u61b6\u69fd\u6578\u91cf\u76f8\u5c0d\u8d8a\u9ad8\uff0c\u5176\u4e2d\u5169\u5169\u76f8\u5c0d\u7684\u95dc\u4fc2\u8a08\u7b97\u4e5f\u6703\u5927\u91cf\u589e\u9577\u3002\u5c07\u95dc\u4fc2\u63d0\u53d6\u51fa\u91cd\u8981 82900 \u51fa\u6a21\u7d44\u4e0d\u9700\u8981\u5c07\u6240\u6709\u95dc\u4fc2\u90fd\u8a08\u7b97\u904e\uff0c\u5728\u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u9032\u884c\u4fdd\u5b58\u6642\uff0c\u5373\u53ef\u7be9\u9078\u51fa\u91cd\u8981\u7684\u95dc \u806f\u8cc7\u8a0a\u9032\u884c\u4fdd\u5b58\uff0c\u4e26\u975e\u6240\u6709\u95dc\u4fc2\u6578\u503c\u90fd\u9700\u8981\u88ab\u4fdd\u5b58\uff0c\u8f38\u51fa\u53ef\u4ee5\u53ea\u5c08\u6ce8\u65bc\u91cd\u8981\u7684\u8cc7\u8a0a\u63a8\u7406 \u7b54\u6848\u3002\u6b64\u5be6\u9a57\u5617\u8a66\u5c07\u8a18\u61b6\u95dc\u806f\u76f4\u63a5\u66f4\u65b0\u65bc\u539f\u8a18\u61b6\u69fd\u5167\uff0c\u800c\u4e0d\u53e6\u5916\u900f\u904e\u95dc\u4fc2\u69fd\u4fdd\u5b58\u95dc\u4fc2\u8cc7 \u8a0a\uff0c\u5be6\u9a57\u662f\u5426\u900f\u904e\u8a18\u61b6\u7684\u81ea\u6211\u95dc\u806f\u66f4\u65b0\uff0c\u5373\u53ef\u63d0\u5347\u8a18\u61b6\u69fd\u4fdd\u5b58\u5167\u5bb9\u7684\u54c1\u8cea\u3002 \u5be6\u9a57\u5167\u5bb9\uff1a \u95dc\u806f\u8a08\u7b97\u65b9\u6cd5\u540c\u5be6\u9a57\u4e8c\uff0c\u5dee\u5225\u5728\u65bc\u5be6\u9a57\u4e09\u66f4\u65b0\u7684\u76ee\u6a19\uff0c\u70ba\u8a18\u61b6\u69fd\u672c\u8eab\u6240\u4fdd\u5b58\u7684\u5167\u5bb9\uff0c\u539f Task 10: Indefinite Knowledge 0.50% 3.70% 0.80% 3.80% Task 11: Basic Coreference 8.90% 8.00% 4.20% 7.50% Task 12: Conjunction 0.00% 0.00% 0.00% 0.60% Task 13: Compound Coreference 5.60% 5.60% 6.20% Task 14: Time Reasoning 20.50% 21.30% 20.60% Task 15: Basic Deduction 5.10% 29.70% 0.00% Task 16: Basic Induction 79500 69500 \u95dc\u4fc2\u69fd\u4e2d\uff0c\u5373\u4f7f\u662f\u5c0f\u578b\u4efb\u52d9\u4e5f\u53ef\u4ee5\u767c\u73fe\u8a08\u7b97\u91cf\u5927\u5e45\u4e0b\u964d\u3002\u800c\u5728\u8d8a\u5927\u578b\u81ea\u7136\u8a9e\u8a00\u4efb\u52d9\u6240\u904b 45.80% Task 15: Basic Deduction 79700 69700 \u5be6\u9a57\u4e2d\u6240\u63a1\u7528\u7684\u554f\u7b54\u4efb\u52d9\u4f7f\u7528 20 \u500b\u8a18\u61b6\u69fd\uff0c\u95dc\u4fc2\u7684\u7e2e\u6e1b\u5f9e 190 \u500b\u95dc\u4fc2\u63d0\u53d6\u5230 20 \u500b 55.90% Task 14: Time Reasoning 81700 71700 \u8a08\u7b97\u91cf\u3002\u672c\u8ad6\u6587\u63d0\u51fa\u95dc\u4fc2\u63d0\u53d6\u7684\u6982\u5ff5\u53ef\u4ee5\u5927\u91cf\u6e1b\u5c11\u6b0a\u91cd\u7684\u8a08\u7b97\u91cf\u3002 5.80% Task 11: Basic Coreference 81600 Task 12: Conjunction 80400 Task 13: Compound Coreference 81600 \u6982\u5ff5\u5e36\u5165\u8a18\u61b6\u7db2\u8def\u4e2d\uff0c\u63d0\u5347\u6a21\u578b\u7684\u6e96\u78ba\u6027\uff0c\u4f46\u5176\u7f3a\u9ede\u4e5f\u5f88\u660e\u986f:\u5927\u91cf\u7684\u63d0\u9ad8\u6a21\u578b\u7684\u6b0a\u91cd\u8207 71600 \u900f\u904e\u4e0d\u540c\u7684\u8a18\u61b6\u4fdd\u5b58\u8207\u63a8\u7406\u65b9\u5f0f\uff0c\u63d0\u5347\u6a21\u578b\u7684\u9577\u671f\u8a18\u61b6\u80fd\u529b\uff0cRelNet \u6a21\u578b\u9996\u5148\u5c07\u95dc\u4fc2\u7684 70400 (Visual Question Answering, VQA)\uff0c\u8a08\u7b97\u5169\u5169\u7269\u9ad4\u9593\u7684\u95dc\u4fc2\u3002\u800c\u8a18\u61b6\u7db2\u8def\u7684\u6982\u5ff5\u76ee\u7684\u5728\u65bc 71600 \u5148\u7531 Google Deepmind \u5718\u968a\u65bc\u8ad6\u6587(Santoro et al., 2017)\u4e2d\u6240\u63d0\u51fa\uff0c\u61c9\u7528\u65bc\u5716\u50cf\u554f\u7b54\u4efb\u52d9 \u5b58\uff0c\u76ee\u7684\u53ea\u4fdd\u5b58\u91cd\u8981\u7684\u8cc7\u8a0a\u3002\u5982\u6b64\u53ef\u4ee5\u5927\u70ba\u6e1b\u5c11\u6a21\u578b\u6b0a\u91cd\u7684\u6578\u91cf 6 \u842c\u500b\uff0c\u8f03\u539f\u4f7f\u7528\u6240\u6709 \u5be6\u9a57\u76ee\u7684\uff1a \u5be6\u9a57\u4e8c\u4e2d\u5c07\u8a08\u7b97\u51fa\u7684 190 \u500b\u95dc\u4fc2\u63d0\u53d6\u70ba\u8207\u8a18\u61b6\u69fd\u6578\u91cf\u76f8\u7b49\u7684\u95dc\u4fc2\u69fd\uff0c\u900f\u904e\u9019\u6a23\u7684\u65b9\u5f0f\u8f38 Task 8: Lists/Sets 1.30% 2.20% 1.70% 9.10% Task 9: Simple Negation 0.40% 0.00% 0.00% Task 10: Indefinite Knowledge 81500 71500 \u7368\u4fdd\u5b58\u5404\u81ea\u7684\u8a0a\u606f\u3002\u4f8b\u5982\u5be6\u9ad4\u7684\u8a0a\u606f\u6216\u5c6c\u6027\u53ef\u4ee5\u806f\u60f3\u5230\u5176\u4ed6\u5be6\u9ad4\u6216\u4e8b\u4ef6\u3002\u95dc\u4fc2\u7684\u6982\u5ff5\u9996 35.60% Task 9: Simple Negation 81100 \u672c\u8ad6\u6587\u900f\u904e\u95dc\u4fc2\u7684\u8a08\u7b97\u4f7f\u8a18\u61b6\u5167\u4e0d\u540c\u8a18\u61b6\u69fd\u5177\u6709\u95dc\u806f\uff0c\u5982\u540c\u4eba\u985e\u8a18\u61b6\u4e2d\u4e0d\u540c\u5be6\u9ad4\u4e26\u975e\u55ae 71100 \u65b9\u6cd5\u3002\u5c0d\u65bc\u6b0a\u91cd\u7684\u4f7f\u7528\u91cf\uff0c\u6b0a\u91cd\u6578\u91cf\u8d8a\u591a\u4ee3\u8868\u6240\u9700\u8981 GPU \u6240\u9700\u8981\u7684\u8a08\u7b97\u91cf\u8d8a\u5927\u3002\u672c\u5be6\u9a57 Task 15: Basic Deduction 109700 79700 Task 16: Basic Induction 109500 79500 Task 17: Positional Reasoning 111100 81100 Task 18: Size Reasoning 110700 80700 Task 19: Path Finding 113200 \u8868 6. \u539f\u6a21\u578b\u8207\u591a\u8df3\u8e8d\u3001\u95dc\u4fc2\u8a08\u7b97\u3001\u81ea\u6211\u95dc\u806f\u66f4\u65b0\u5be6\u9a57\u7d50\u679c(\u932f\u8aa4\u7387) [Table 6. Error rate of different models] Task Original model Multi hop (hop2) Relation slot Self \u5f9e\u5be6\u9a57\u6578\u64da\u4e2d\u53ef\u770b\u51fa\u5be6\u9a57\u4e8c\u7684\u6539\u5584\u6548\u679c\u8f03\u70ba\u660e\u986f\uff0c\u7279\u5225\u662f\u4efb\u52d9 2 \u7684\u6e96\u78ba\u7387\u5927\u5e45\u63d0\u5347\u3002 \u5be6\u9a57\u4e09\u6548\u679c\u4e0b\u964d\u6700\u591a\u3002\u5c07\u95dc\u806f\u8a08\u7b97\u8207\u672c\u8eab\u4fdd\u5b58\u7684\u8a18\u61b6\u540c\u6642\u66f4\u65b0\u65bc\u540c\u500b\u8a18\u61b6\u69fd\uff0c\u53cd\u800c \u5927\u91cf\u6587\u672c\u8cc7\u6599\u4f86\u8a13\u7df4\u81ea\u7136\u8a9e\u8a00\u8a5e\u53e5\u7684\u95dc\u4fc2\u3002\u900f\u904e\u672a\u6a19\u8a3b\u7684\u5927\u91cf\u8cc7\u6599\u8a13\u7df4\uff0c\u4f7f\u7de8\u78bc\u7684\u5411\u91cf \u53ef\u4ee5\u66f4\u6e96\u78ba\u7684\u8868\u793a\u8a5e\u53e5\u7684\u610f\u601d\u3002 \u8868 7Task Relation slot Self memory update \u9020\u6210\u6a21\u578b\u6574\u9ad4\u6548\u679c\u4e0b\u964d\u3002\u63a8\u8ad6\u5c07\u95dc\u806f\u8a08\u7b97\u66f4\u65b0\u8a18\u61b6\u69fd\uff0c\u6703\u9020\u6210\u8a18\u61b6\u4fdd\u5b58\u7684\u6df7\u4e82\u3002\u76ee\u524d\u65b9 \u672c\u7814\u7a76\u7684\u5be6\u9a57\u7de8\u78bc\u65b9\u5f0f\u90fd\u662f\u8207\u6574\u9ad4\u6a21\u578b\u4e00\u8d77\u8a13\u7df4\uff0c\u5305\u542b\u7de8\u78bc\u3001\u52d5\u614b\u8a18\u61b6\u6a21\u7d44\u4ee5\u53ca\u63a8 memory Task 1: Single Supporting Fact 0.00% 0.00% 0.00% Task 1: Single Supporting Fact 80000 70000 \u6cd5\u4ecd\u662f\u9700\u8981\u5206\u958b\u4fdd\u5b58\u95dc\u806f\u8cc7\u8a0a\u8207\u8a18\u61b6\u672c\u8eab\uff0c\u4f46\u4e5f\u4e0d\u4ee3\u8868\u8a18\u61b6\u7684\u95dc\u4fc2\u81ea\u6211\u66f4\u65b0\u4e0d\u53ef\u884c\uff0c\u800c \u7406\u6a21\u7d44\u7684\u6b0a\u91cd\uff0c\u6578\u64da\u91cf\u7684\u4e0d\u8db3\u8f03\u7121\u6cd5\u6df1\u5165\u5b78\u7fd2\u8a5e\u53e5\u610f\u6db5\uff0c\u800c\u76ee\u524d\u7db2\u8def\u6587\u672c\u8cc7\u6599\u91cf\u5927\uff0c\u672a 0.00% Task 2: Two Supporting Facts 82900 72900 \u662f\u9700\u8981\u8a73\u7d30\u7814\u7a76\u8a18\u61b6\u69fd\u8207\u95dc\u806f\u69fd\u7684\u5167\u5bb9\u8207\u7279\u6027\uff0c\u5f9e\u800c\u627e\u51fa\u66f4\u597d\u7684\u8a18\u61b6\u4fdd\u5b58\u65b9\u6cd5\u3002 \u4f86\u53ef\u5c07\u6a21\u578b\u7684 Encoder \u6a21\u7d44\u7d93\u904e\u9810\u8a13\u7df4\uff0c\u63d0\u5347\u7de8\u78bc\u6548\u679c\uff0c\u6216\u662f\u91dd\u5c0d\u7de8\u78bc\u65b9\u5f0f\u505a\u6539\u9032\uff0c\u63d0 83200 Task 20: Agent's Motivations 113600 Task 2: Two Supporting Facts 20.80% 28.40% 11.60% 52.30% Task 3: Three Supporting Facts 83400 73400 \u9ad8\u6574\u9ad4\u6a21\u578b\u9810\u6e2c\u7684\u6548\u679c\u3002 \u5f9e\u6b0a\u91cd\u8a08\u7b97\u91cf\u65b9\u9762\u4f86\u770b\uff0c\u5be6\u9a57\u4e00\u6b0a\u91cd\u4f7f\u7528\u91cf\u6700\u5c11\u3002\u56e0\u6a21\u578b\u5c1a\u672a\u52a0\u9032\u8a18\u61b6\u95dc\u806f\u7684\u8a08\u7b97\uff0c 83600 Sum of all task parameters 2240200 1640200 Mean parameters 112010 Task 7: Counting 10.10% 10.10% 6.90% 23.40% Task 8: Lists/Sets 85100 75100 5. \u7d50\u8ad6 (Conclusions) 4.3 \u5be6\u9a57\u4e09(\u81ea\u6211\u8a18\u61b6\u95dc\u806f) (Self memory Relation) Task 5: Three Argument Relations 1.20% 1.20% 1.40% 17.20% Task 6: Yes/No Questions 3.60% 3.50% 1.90% 11.40% Task 6: Yes/No Questions 83400 Task 7: Counting 85100 75100 \u6574\u9ad4\u4efb\u52d9\u53c8\u4e0b\u964d\u4e86 20 \u842c\u6b0a\u91cd\u3002 73400 60 \u842c\u6b0a\u91cd\u6578\u91cf\uff0c\u8f03\u5be6\u9a57\u4e00\u4e0b\u964d\u4e86 26.8%\u7684\u6b0a\u91cd\u91cf\u3002\u5be6\u9a57\u4e09\u96d6\u6e96\u78ba\u7387\u4e0d\u9ad8\uff0c\u4f46\u76f8\u8f03\u5be6\u9a57\u4e8c 82010 Task 3: Three Supporting Facts 58.70% 56.70% 62.90% 62.10% Task 4: Two Argument Relations 0.10% 0.20% 0.00% 0.00% Task 4: Two Argument Relations 79400 \u4e14\u56e0\u6a21\u578b\u91cd\u8907\u4f7f\u7528\u76f8\u540c\u6ce8\u610f\u529b\u6a5f\u5236\u91cd\u8907\u8a08\u7b97\uff0c\u6b0a\u91cd\u7528\u91cf\u8207\u539f\u6a21\u578b\u5dee\u7570\u4e0d\u5927\u3002 69400 Task 5: Three Argument Relations 85200 75200 \u800c\u5be6\u9a57\u4e8c\u95dc\u806f\u63d0\u53d6\u8207\u6240\u6709\u95dc\u806f\u8a08\u7b97\u6bd4\u8f03\uff0c\u6b0a\u91cd\u4e0b\u964d\u5e45\u5ea6\u6700\u591a\uff0c\u6240\u6709\u4efb\u52d9\u6574\u9ad4\u4e0b\u964d\u4e86 \u53c3\u8003\u6587\u737b(References)</td></tr></table>", |
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| "text": "Neural Semantic Encoders, NSE)(Munkhdalai & Yu, 2016)\u67b6\u69cb\uff0c \u5728\u904e\u5f80\u591a\u8f2a\u8b80\u53d6\u6a5f\u5236\u591a\u70ba\u56fa\u5b9a\u6b65\u6578\uff0c\u4f46\u4e26\u975e\u6240\u6709\u7684\u554f\u984c\u9700\u8981\u76f8\u540c\u63a8\u7406\u7684\u6b65\u6578\u3002\u6709\u4e9b\u554f\u984c \u53ea\u9700\u8981\u7c21\u55ae\u7684\u8a5e\u53e5\u6bd4\u5c0d\u5373\u53ef\u5f97\u51fa\u7d50\u8ad6\uff0c\u6709\u4e9b\u554f\u984c\u5247\u9700\u8981\u8907\u96dc\u7684\u8a9e\u610f\u7406\u89e3\u8207\u6df1\u5ea6\u63a8\u7406\uff0c\u56e0 \u6b64 NSE \u5229\u7528\u52d5\u614b\u6b65\u6578\u8abf\u6574\u6a21\u578b\u4ee5\u89e3\u6c7a\u6b64\u554f\u984c\u3002 \u6574\u7406\u8ad6\u6587(Sukhbaatar et al., 2015) (Henaff et al., 2017) (Seo et al., 2017)\u5be6\u9a57\u6578\u64da\u4ee5\u8868 \u683c\u5448\u73fe\uff0c\u9996\u5148\u8868 2 \u5be6\u9a57\u5728 bAbI \u6578\u64da\u96c6\u4e0a\uff0c\u4ee5\u4e00\u5343\u7b46\u8cc7\u6599\u8a13\u7df4\uff0c\u900f\u904e\u8868\u683c\u4e2d\u53ef\u767c\u73fe\u900f\u904e\u591a \u8df3\u8e8d\u6a5f\u5236\u53ef\u63d0\u9ad8\u901a\u904e\u7684\u4efb\u52d9\u6578\u91cf\u6216\u662f\u964d\u4f4e\u5e73\u5747\u932f\u8aa4\u7387\uff0c\u4e0d\u540c\u591a\u8df3\u8e8d\u6b65\u6578\u4e5f\u6703\u5f71\u97ff\u7d50\u679c\u3002 \u8868 Cui et al., 2017) (Trischler et al., 2016)\u5169\u7bc7\u8ad6\u6587\u5be6\u9a57\u6240\u63d0\u4f9b\u7684\u8cc7\u6599\u70ba\u57fa\u790e\uff0c\u4f7f\u7528 bAbI \u6578\u64da\u96c6\u4e2d 10k \u6578\u64da\u91cf\u8a13\u7df4\uff0c\u4e26\u5e73\u5747 20 \u9805\u4efb\u52d9\u7684\u932f\u8aa4\u7387\u6bd4\u8f03\u7d50\u679c\u986f\u793a\u65bc\u8868 4\u3002\u7531\u5be6\u9a57 \u53ef\u4ee5\u4e86\u89e3\u5230\uff0c\u52a0\u5165\u95dc\u4fc2\u8a08\u7b97\u80fd\u6709\u6548\u7684\u63d0\u5347\u6a21\u578b\u7684\u8a13\u7df4\u7d50\u679c\u8207\u8a08\u7b97\u3002 \u8868 John likes Mary.\u2260Mary likes John. \u6578\u64da\u7de8\u78bc\u5b8c\u5f8c\u4ee5\u5411\u91cf\u5f62\u5f0f\u8868\u793a\u6bcf\u500b\u53e5\u5b50 \u3002t \u70ba\u4e0d\u540c\u6642\u9593\u6b65\u7684\u53e5\u5b50\uff0c\u4f9d\u7167\u9806\u5e8f\u8f38\u5165\u81f3 \u6a21\u578b\u5167\u66f4\u65b0\u8a18\u61b6\u69fd\u8207\u95dc\u4fc2\u69fd\u3002\u6bcf\u500b\u8a18\u61b6\u69fd\u7531 key \u548c value \u7d44\u6210,\u5206\u5225\u70ba wi \u548c hi \uff0c\u4ee5 key-value \u7684\u5f62\u5f0f\u4fdd\u5b58\u8cc7\u8a0a\u3002key \u8ca0\u8cac\u4fdd\u5b58\u5be6\u9ad4\u3001value \u8ca0\u8cac\u4fdd\u5b58\u72c0\u614b\uff0c\u7bc4\u4f8b\u5982\u4e0b\u65b9\u6240\u793a\u3002\u7bc4\u4f8b\u4e2d key \u4fdd\u5b58\u4e86 John \u9019\u500b\u5be6\u9ad4\uff0cvalue \u4fdd\u5b58\u4e86 John \u6240\u505a\u7684\u52d5\u4f5c\uff0c\u6bcf\u500b\u8a18\u61b6\u69fd\u90fd\u6709\u81ea\u5df1\u7684 key \u8207 value \u5411\u91cf\uff0c\u900f\u904e\u8f38\u5165\u6578\u64da\u8207 key-value \u7684\u6bd4\u5c0d\u53ef\u627e\u5230\u6b64\u6b21\u72c0\u614b\u66f4\u65b0\u61c9\u8a72\u66f4\u65b0\u65bc\u54ea\u500b\u8a18\u61b6\u69fd\u3002", |
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