{"id":477061,"date":"2023-08-09T09:06:59","date_gmt":"2023-08-09T09:06:59","guid":{"rendered":""},"modified":"2023-09-05T11:13:56","modified_gmt":"2023-09-05T11:13:56","slug":"elmo","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/elmo\/","title":{"rendered":"ELMo"},"content":{"rendered":"<p>ELMo \u662f Embeddings from Language Models \u7684\u7f29\u5199\uff0c\u662f\u4e00\u79cd\u7a81\u7834\u6027\u7684\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u8bed\u8a00\u8868\u793a\u6a21\u578b\u3002 ELMo \u7531\u827e\u4f26\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u6240 (AI2) \u7684\u7814\u7a76\u4eba\u5458\u4e8e 2018 \u5e74\u5f00\u53d1\uff0c\u5f7b\u5e95\u6539\u53d8\u4e86\u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP) \u4efb\u52a1\u5e76\u589e\u5f3a\u4e86\u5404\u79cd\u5e94\u7528\u7a0b\u5e8f\uff0c\u5305\u62ec OneProxy \u7b49\u4ee3\u7406\u670d\u52a1\u5668\u63d0\u4f9b\u5546\u3002\u672c\u6587\u5c06\u6df1\u5165\u63a2\u8ba8 ELMo \u7684\u5386\u53f2\u3001\u5185\u90e8\u5de5\u4f5c\u539f\u7406\u3001\u4e3b\u8981\u529f\u80fd\u3001\u7c7b\u578b\u3001\u7528\u4f8b\u548c\u672a\u6765\u524d\u666f\uff0c\u4ee5\u53ca\u5b83\u4e0e\u4ee3\u7406\u670d\u52a1\u5668\u7684\u6f5c\u5728\u5173\u8054\u3002<\/p>\n<h2>ELMo \u7684\u8d77\u6e90\u5386\u53f2\u548c\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>ELMo \u7684\u8d77\u6e90\u53ef\u4ee5\u8ffd\u6eaf\u5230\u5bf9\u66f4\u5177\u4e0a\u4e0b\u6587\u611f\u77e5\u7684\u8bcd\u5d4c\u5165\u7684\u9700\u6c42\u3002\u4f20\u7edf\u7684\u8bcd\u5d4c\u5165\uff0c\u5982 Word2Vec \u548c GloVe\uff0c\u5c06\u6bcf\u4e2a\u8bcd\u89c6\u4e3a\u4e00\u4e2a\u72ec\u7acb\u7684\u5b9e\u4f53\uff0c\u800c\u5ffd\u7565\u5468\u56f4\u7684\u4e0a\u4e0b\u6587\u3002\u7136\u800c\uff0c\u7814\u7a76\u4eba\u5458\u53d1\u73b0\uff0c\u5355\u8bcd\u7684\u542b\u4e49\u53ef\u80fd\u4f1a\u6839\u636e\u5176\u5728\u53e5\u5b50\u4e2d\u7684\u4e0a\u4e0b\u6587\u800c\u6709\u5f88\u5927\u5dee\u5f02\u3002<\/p>\n<p>\u9996\u6b21\u63d0\u53ca ELMo \u662f\u5728 Matthew Peters \u7b49\u4eba\u4e8e 2018 \u5e74\u53d1\u8868\u7684\u9898\u4e3a\u201cDeep contextualized wordrepresentation\u201d\u7684\u8bba\u6587\u4e2d\u3002\u8be5\u8bba\u6587\u4ecb\u7ecd\u4e86 ELMo \u4f5c\u4e3a\u4e00\u79cd\u4f7f\u7528\u53cc\u5411\u8bed\u8a00\u6a21\u578b\u751f\u6210\u4e0a\u4e0b\u6587\u76f8\u5173\u8bcd\u5d4c\u5165\u7684\u65b0\u65b9\u6cd5\u3002<\/p>\n<h2>\u6709\u5173 ELMo \u7684\u8be6\u7ec6\u4fe1\u606f\u3002\u6269\u5c55\u4e3b\u9898 ELMo\u3002<\/h2>\n<p>ELMo \u901a\u8fc7\u5229\u7528\u53cc\u5411\u8bed\u8a00\u6a21\u578b\u7684\u529b\u91cf\uff0c\u5229\u7528\u6df1\u5ea6\u4e0a\u4e0b\u6587\u5316\u7684\u5355\u8bcd\u8868\u793a\u65b9\u6cd5\u3002\u4f20\u7edf\u8bed\u8a00\u6a21\u578b\uff0c\u5982 LSTM\uff08\u957f\u77ed\u671f\u8bb0\u5fc6\uff09\uff0c\u4ece\u5de6\u5230\u53f3\u5904\u7406\u53e5\u5b50\uff0c\u6355\u83b7\u8fc7\u53bb\u5355\u8bcd\u7684\u4f9d\u8d56\u5173\u7cfb\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0cELMo \u7ed3\u5408\u4e86\u524d\u5411\u548c\u540e\u5411 LSTM\uff0c\u5141\u8bb8\u6a21\u578b\u5728\u521b\u5efa\u8bcd\u5d4c\u5165\u65f6\u8003\u8651\u6574\u4e2a\u53e5\u5b50\u4e0a\u4e0b\u6587\u3002<\/p>\n<p>ELMo \u7684\u4f18\u52bf\u5728\u4e8e\u5b83\u80fd\u591f\u6839\u636e\u5468\u56f4\u7684\u5355\u8bcd\u4e3a\u6bcf\u4e2a\u5b9e\u4f8b\u751f\u6210\u52a8\u6001\u5355\u8bcd\u8868\u793a\u3002\u5b83\u89e3\u51b3\u4e86\u4e00\u8bcd\u591a\u4e49\u7684\u95ee\u9898\uff0c\u5373\u4e00\u4e2a\u8bcd\u53ef\u4ee5\u6709\u591a\u79cd\u542b\u4e49\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u5176\u4e0a\u4e0b\u6587\u3002\u901a\u8fc7\u5b66\u4e60\u4e0a\u4e0b\u6587\u76f8\u5173\u7684\u8bcd\u5d4c\u5165\uff0cELMo \u663e\u7740\u63d0\u9ad8\u4e86\u5404\u79cd NLP \u4efb\u52a1\u7684\u6027\u80fd\uff0c\u4f8b\u5982\u60c5\u611f\u5206\u6790\u3001\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u548c\u8bcd\u6027\u6807\u6ce8\u3002<\/p>\n<h2>ELMo\u7684\u5185\u90e8\u7ed3\u6784\u3002 ELMo \u7684\u5de5\u4f5c\u539f\u7406\u3002<\/h2>\n<p>ELMo\u7684\u5185\u90e8\u7ed3\u6784\u57fa\u4e8e\u6df1\u5ea6\u53cc\u5411\u8bed\u8a00\u6a21\u578b\u3002\u5b83\u7531\u4e24\u4e2a\u5173\u952e\u7ec4\u4ef6\u7ec4\u6210\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u57fa\u4e8e\u5b57\u7b26\u7684\u5355\u8bcd\u8868\u793a\uff1a<\/strong> ELMo \u9996\u5148\u4f7f\u7528\u5b57\u7b26\u7ea7 CNN\uff08\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff09\u5c06\u6bcf\u4e2a\u5355\u8bcd\u8f6c\u6362\u4e3a\u57fa\u4e8e\u5b57\u7b26\u7684\u8868\u793a\u3002\u8fd9\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5904\u7406\u8bcd\u6c47\u5916 (OOV) \u5355\u8bcd\u5e76\u6709\u6548\u6355\u83b7\u5b50\u8bcd\u4fe1\u606f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u53cc\u5411 LSTM\uff1a<\/strong> \u83b7\u5f97\u57fa\u4e8e\u5b57\u7b26\u7684\u5355\u8bcd\u8868\u793a\u540e\uff0cELMo \u5c06\u5b83\u4eec\u8f93\u5165\u4e24\u5c42\u53cc\u5411 LSTM\u3002\u7b2c\u4e00\u4e2a LSTM \u4ece\u5de6\u5230\u53f3\u5904\u7406\u53e5\u5b50\uff0c\u800c\u7b2c\u4e8c\u4e2a LSTM \u4ece\u53f3\u5230\u5de6\u5904\u7406\u53e5\u5b50\u3002\u5c06\u4e24\u4e2a LSTM \u7684\u9690\u85cf\u72b6\u6001\u8fde\u63a5\u8d77\u6765\u4ee5\u521b\u5efa\u6700\u7ec8\u7684\u8bcd\u5d4c\u5165\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u7136\u540e\uff0c\u751f\u6210\u7684\u4e0a\u4e0b\u6587\u5d4c\u5165\u5c06\u7528\u4f5c\u4e0b\u6e38 NLP \u4efb\u52a1\u7684\u8f93\u5165\uff0c\u4e0e\u4f20\u7edf\u7684\u9759\u6001\u8bcd\u5d4c\u5165\u76f8\u6bd4\uff0c\u6027\u80fd\u663e\u7740\u63d0\u5347\u3002<\/p>\n<h2>ELMo \u7684\u5173\u952e\u7279\u6027\u5206\u6790\u3002<\/h2>\n<p>ELMo \u62e5\u6709\u51e0\u4e2a\u4e0e\u4f20\u7edf\u8bcd\u5d4c\u5165\u4e0d\u540c\u7684\u5173\u952e\u7279\u6027\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u4e0a\u4e0b\u6587\u654f\u611f\u6027\uff1a<\/strong> ELMo \u6355\u83b7\u5355\u8bcd\u7684\u4e0a\u4e0b\u6587\u4fe1\u606f\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u51c6\u786e\u3001\u66f4\u6709\u610f\u4e49\u7684\u5355\u8bcd\u5d4c\u5165\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4e00\u8bcd\u591a\u4e49\u5904\u7406\uff1a<\/strong> \u901a\u8fc7\u8003\u8651\u6574\u4e2a\u53e5\u5b50\u4e0a\u4e0b\u6587\uff0cELMo \u514b\u670d\u4e86\u9759\u6001\u5d4c\u5165\u7684\u5c40\u9650\u6027\uff0c\u5e76\u5904\u7406\u591a\u4e49\u8bcd\u7684\u591a\u91cd\u542b\u4e49\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8bcd\u6c47\u5916 (OOV) \u652f\u6301\uff1a<\/strong> ELMo \u57fa\u4e8e\u5b57\u7b26\u7684\u65b9\u6cd5\u4f7f\u5176\u80fd\u591f\u6709\u6548\u5904\u7406 OOV \u5355\u8bcd\uff0c\u786e\u4fdd\u73b0\u5b9e\u573a\u666f\u4e2d\u7684\u7a33\u5065\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8fc1\u79fb\u5b66\u4e60\uff1a<\/strong> \u9884\u8bad\u7ec3\u7684 ELMo \u6a21\u578b\u53ef\u4ee5\u9488\u5bf9\u7279\u5b9a\u7684\u4e0b\u6e38\u4efb\u52a1\u8fdb\u884c\u5fae\u8c03\uff0c\u4ece\u800c\u5b9e\u73b0\u9ad8\u6548\u7684\u8fc1\u79fb\u5b66\u4e60\u5e76\u51cf\u5c11\u8bad\u7ec3\u65f6\u95f4\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6700\u5148\u8fdb\u7684\u6027\u80fd\uff1a<\/strong> ELMo \u5728\u5404\u79cd NLP \u57fa\u51c6\u6d4b\u8bd5\u4e2d\u5c55\u793a\u4e86\u6700\u5148\u8fdb\u7684\u6027\u80fd\uff0c\u5c55\u793a\u4e86\u5176\u591a\u529f\u80fd\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5199\u51fa\u5b58\u5728\u54ea\u4e9b\u7c7b\u578b\u7684 ELMo\u3002\u4f7f\u7528\u8868\u683c\u548c\u5217\u8868\u6765\u5199\u4f5c\u3002<\/h2>\n<p>\u6839\u636e\u4e0a\u4e0b\u6587\u8868\u793a\uff0cELMo \u6a21\u578b\u4e3b\u8981\u6709\u4e24\u79cd\u7c7b\u578b\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u7c7b\u578b<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u539f\u88c5ELMo<\/td>\n<td>\u8be5\u6a21\u578b\u57fa\u4e8e\u53cc\u5411 LSTM \u751f\u6210\u4e0a\u4e0b\u6587\u76f8\u5173\u7684\u8bcd\u5d4c\u5165\u3002\u5b83\u63d0\u4f9b\u57fa\u4e8e\u6574\u4e2a\u53e5\u5b50\u4e0a\u4e0b\u6587\u7684\u5355\u8bcd\u8868\u793a\u3002<\/td>\n<\/tr>\n<tr>\n<td>ELMo 2.0<\/td>\n<td>\u8be5\u6a21\u578b\u4ee5\u539f\u59cb ELMo \u4e3a\u57fa\u7840\uff0c\u9664\u4e86\u53cc\u5411 LSTM \u4e4b\u5916\uff0c\u8fd8\u7ed3\u5408\u4e86\u81ea\u6ce8\u610f\u529b\u673a\u5236\u3002\u5b83\u8fdb\u4e00\u6b65\u7ec6\u5316\u4e86\u4e0a\u4e0b\u6587\u5d4c\u5165\uff0c\u63d0\u9ad8\u4e86\u67d0\u4e9b\u4efb\u52a1\u7684\u6027\u80fd\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ELMo\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u4f7f\u7528\u4e2d\u9047\u5230\u7684\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\u3002<\/h2>\n<p>ELMo \u5728\u5404\u79cd NLP \u4efb\u52a1\u4e2d\u90fd\u6709\u5e94\u7528\uff0c\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u60c5\u7eea\u5206\u6790\uff1a<\/strong> ELMo \u7684\u60c5\u5883\u5316\u5d4c\u5165\u6709\u52a9\u4e8e\u6355\u6349\u5fae\u5999\u7684\u60c5\u7eea\u548c\u60c5\u7eea\uff0c\u4ece\u800c\u5f62\u6210\u66f4\u51c6\u786e\u7684\u60c5\u7eea\u5206\u6790\u6a21\u578b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff08NER\uff09\uff1a<\/strong> NER \u7cfb\u7edf\u53d7\u76ca\u4e8e ELMo \u6839\u636e\u5468\u56f4\u4e0a\u4e0b\u6587\u6d88\u9664\u5b9e\u4f53\u63d0\u53ca\u6b67\u4e49\u7684\u80fd\u529b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u95ee\u9898\u89e3\u7b54\uff1a<\/strong> ELMo \u6709\u52a9\u4e8e\u7406\u89e3\u95ee\u9898\u548c\u6bb5\u843d\u7684\u4e0a\u4e0b\u6587\uff0c\u63d0\u9ad8\u95ee\u7b54\u7cfb\u7edf\u7684\u6027\u80fd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u673a\u5668\u7ffb\u8bd1\uff1a<\/strong> ELMo \u7684\u4e0a\u4e0b\u6587\u611f\u77e5\u5355\u8bcd\u8868\u793a\u63d0\u9ad8\u4e86\u673a\u5668\u7ffb\u8bd1\u6a21\u578b\u7684\u7ffb\u8bd1\u8d28\u91cf\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u7136\u800c\uff0c\u4f7f\u7528 ELMo \u53ef\u80fd\u4f1a\u5e26\u6765\u4e00\u4e9b\u6311\u6218\uff1a<\/p>\n<ul>\n<li>\n<p><strong>\u8ba1\u7b97\u6210\u672c\u9ad8\uff1a<\/strong> \u7531\u4e8e\u5176\u6df1\u5c42\u67b6\u6784\u548c\u53cc\u5411\u5904\u7406\uff0cELMo \u9700\u8981\u5927\u91cf\u8ba1\u7b97\u8d44\u6e90\u3002\u8fd9\u53ef\u80fd\u4f1a\u7ed9\u8d44\u6e90\u6709\u9650\u7684\u73af\u5883\u5e26\u6765\u6311\u6218\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u63a8\u7406\u65f6\u95f4\u957f\uff1a<\/strong> \u751f\u6210 ELMo \u5d4c\u5165\u53ef\u80fd\u975e\u5e38\u8017\u65f6\uff0c\u4f1a\u5f71\u54cd\u5b9e\u65f6\u5e94\u7528\u7a0b\u5e8f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u96c6\u6210\u590d\u6742\u6027\uff1a<\/strong> \u5c06 ELMo \u7eb3\u5165\u73b0\u6709\u7684 NLP \u6d41\u7a0b\u53ef\u80fd\u9700\u8981\u989d\u5916\u7684\u52aa\u529b\u548c\u9002\u5e94\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u4e3a\u4e86\u7f13\u89e3\u8fd9\u4e9b\u6311\u6218\uff0c\u7814\u7a76\u4eba\u5458\u548c\u4ece\u4e1a\u8005\u63a2\u7d22\u4e86\u4f18\u5316\u6280\u672f\u3001\u6a21\u578b\u84b8\u998f\u548c\u786c\u4ef6\u52a0\u901f\uff0c\u4ee5\u4f7f ELMo \u66f4\u6613\u4e8e\u4f7f\u7528\u548c\u9ad8\u6548\u3002<\/p>\n<h2>\u4ee5\u8868\u683c\u548c\u5217\u8868\u7684\u5f62\u5f0f\u5217\u51fa\u4e3b\u8981\u7279\u5f81\u4ee5\u53ca\u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83\u3002<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>ELMo<\/th>\n<th>\u8bcd\u5411\u91cf<\/th>\n<th>\u624b\u5957<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u4e0a\u4e0b\u6587\u654f\u611f\u6027<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u4e0d<\/td>\n<td>\u4e0d<\/td>\n<\/tr>\n<tr>\n<td>\u4e00\u8bcd\u591a\u4e49\u5904\u7406<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u4e0d<\/td>\n<td>\u4e0d<\/td>\n<\/tr>\n<tr>\n<td>\u8bcd\u6c47\u5916 (OOV)<\/td>\n<td>\u51fa\u8272\u7684<\/td>\n<td>\u6709\u9650\u7684<\/td>\n<td>\u6709\u9650\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u8fc1\u79fb\u5b66\u4e60<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u9884\u8bad\u7ec3\u6570\u636e\u5927\u5c0f<\/td>\n<td>\u5927\u7684<\/td>\n<td>\u4e2d\u7b49\u7684<\/td>\n<td>\u5927\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u8bad\u7ec3\u65f6\u95f4<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u4f4e\u7684<\/td>\n<td>\u4f4e\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u578b\u53f7\u5c3a\u5bf8<\/td>\n<td>\u5927\u7684<\/td>\n<td>\u5c0f\u7684<\/td>\n<td>\u4e2d\u7b49\u7684<\/td>\n<\/tr>\n<tr>\n<td>NLP \u4efb\u52a1\u7684\u8868\u73b0<\/td>\n<td>\u6700\u5148\u8fdb\u7684<\/td>\n<td>\u7f13\u548c<\/td>\n<td>\u597d\u7684<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e ELMo \u76f8\u5173\u7684\u672a\u6765\u524d\u666f\u548c\u6280\u672f\u3002<\/h2>\n<p>\u4e0e\u4efb\u4f55\u5feb\u901f\u53d1\u5c55\u7684\u9886\u57df\u4e00\u6837\uff0cELMo \u7684\u672a\u6765\u5145\u6ee1\u5e0c\u671b\u3002\u4e00\u4e9b\u6f5c\u5728\u7684\u53d1\u5c55\u5305\u62ec\uff1a<\/p>\n<ul>\n<li>\n<p><strong>\u6548\u7387\u63d0\u5347\uff1a<\/strong> \u7814\u7a76\u4eba\u5458\u53ef\u80fd\u4f1a\u4e13\u6ce8\u4e8e\u4f18\u5316 ELMo \u7684\u67b6\u6784\uff0c\u4ee5\u964d\u4f4e\u8ba1\u7b97\u6210\u672c\u548c\u63a8\u7406\u65f6\u95f4\uff0c\u4f7f\u5176\u66f4\u6613\u4e8e\u66f4\u5e7f\u6cdb\u7684\u5e94\u7528\u7a0b\u5e8f\u4f7f\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u8bed\u8a00\u652f\u6301\uff1a<\/strong> \u6269\u5c55 ELMo \u5904\u7406\u591a\u79cd\u8bed\u8a00\u7684\u80fd\u529b\u5c06\u4e3a\u8de8\u8bed\u8a00 NLP \u4efb\u52a1\u91ca\u653e\u65b0\u7684\u53ef\u80fd\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6301\u7eed\u5b66\u4e60\uff1a<\/strong> \u6301\u7eed\u5b66\u4e60\u6280\u672f\u7684\u8fdb\u6b65\u53ef\u80fd\u4f7f ELMo \u80fd\u591f\u9010\u6b65\u9002\u5e94\u65b0\u6570\u636e\u5e76\u4ece\u4e2d\u5b66\u4e60\uff0c\u786e\u4fdd\u5176\u4e0e\u4e0d\u65ad\u53d1\u5c55\u7684\u8bed\u8a00\u6a21\u5f0f\u4fdd\u6301\u540c\u6b65\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6a21\u578b\u538b\u7f29\uff1a<\/strong> \u53ef\u4ee5\u5e94\u7528\u6a21\u578b\u84b8\u998f\u548c\u91cf\u5316\u7b49\u6280\u672f\u6765\u521b\u5efa ELMo \u7684\u8f7b\u91cf\u7ea7\u7248\u672c\uff0c\u800c\u4e0d\u4f1a\u727a\u7272\u592a\u591a\u6027\u80fd\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5982\u4f55\u5c06\u4ee3\u7406\u670d\u52a1\u5668\u4e0e ELMo \u5173\u8054\u3002<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4ece ELMo \u4e2d\u53d7\u76ca\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u589e\u5f3a\u7684\u5185\u5bb9\u8fc7\u6ee4\uff1a<\/strong> ELMo \u7684\u4e0a\u4e0b\u6587\u5d4c\u5165\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7406\u670d\u52a1\u5668\u4e2d\u4f7f\u7528\u7684\u5185\u5bb9\u8fc7\u6ee4\u7cfb\u7edf\u7684\u51c6\u786e\u6027\uff0c\u4ece\u800c\u66f4\u597d\u5730\u8bc6\u522b\u4e0d\u9002\u5f53\u6216\u6709\u5bb3\u7684\u5185\u5bb9\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8bed\u8a00\u611f\u77e5\u8def\u7531\uff1a<\/strong> ELMo \u53ef\u4ee5\u534f\u52a9\u8bed\u8a00\u611f\u77e5\u8def\u7531\uff0c\u786e\u4fdd\u7528\u6237\u8bf7\u6c42\u88ab\u5b9a\u5411\u5230\u5177\u6709\u6700\u76f8\u5173\u8bed\u8a00\u5904\u7406\u80fd\u529b\u7684\u4ee3\u7406\u670d\u52a1\u5668\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5f02\u5e38\u68c0\u6d4b\uff1a<\/strong> \u901a\u8fc7\u4f7f\u7528 ELMo \u5206\u6790\u7528\u6237\u884c\u4e3a\u548c\u8bed\u8a00\u6a21\u5f0f\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u66f4\u597d\u5730\u68c0\u6d4b\u548c\u9632\u6b62\u53ef\u7591\u6d3b\u52a8\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u8bed\u8a00\u4ee3\u7406\uff1a<\/strong> ELMo \u7684\u591a\u8bed\u8a00\u652f\u6301\uff08\u5982\u679c\u5c06\u6765\u53ef\u7528\uff09\u5c06\u4f7f\u4ee3\u7406\u670d\u52a1\u5668\u80fd\u591f\u66f4\u6709\u6548\u5730\u5904\u7406\u5404\u79cd\u8bed\u8a00\u7684\u5185\u5bb9\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u603b\u4f53\u800c\u8a00\uff0c\u5c06 ELMo \u96c6\u6210\u5230\u4ee3\u7406\u670d\u52a1\u5668\u57fa\u7840\u8bbe\u65bd\u4e2d\u53ef\u4ee5\u63d0\u9ad8\u6027\u80fd\u3001\u589e\u5f3a\u5b89\u5168\u6027\u548c\u66f4\u52a0\u65e0\u7f1d\u7684\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173 ELMo \u53ca\u5176\u5e94\u7528\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u8bf7\u53c2\u9605\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ol>\n<li><a href=\"https:\/\/allennlp.org\/elmo\" target=\"_new\" rel=\"noopener nofollow\">ELMo\uff1a\u8bed\u8a00\u6a21\u578b\u7684\u5d4c\u5165<\/a><\/li>\n<li><a href=\"https:\/\/www.aclweb.org\/anthology\/N18-1202.pdf\" target=\"_new\" rel=\"noopener nofollow\">\u539f\u88c5ELMo\u7eb8<\/a><\/li>\n<li><a href=\"https:\/\/www.aclweb.org\/anthology\/P19-1613.pdf\" target=\"_new\" rel=\"noopener nofollow\">ELMo 2.0\uff1a\u7f3a\u5c11\u9884\u8bad\u7ec3<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/allenai\/allennlp\/blob\/main\/tutorials\/how_to\/elmo.md\" target=\"_new\" rel=\"noopener nofollow\">AI2 \u7684 ELMo \u6559\u7a0b<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468299,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477061","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>ELMo: Empowering Language Models for Proxy Server Providers<\/mark>","faq_items":[{"question":"What is ELMo?","answer":"<p>ELMo, short for Embeddings from Language Models, is a deep learning-based language representation model developed by the Allen Institute for Artificial Intelligence (AI2) in 2018. It generates context-sensitive word embeddings by using bidirectional language models, revolutionizing various natural language processing (NLP) tasks.<\/p>"},{"question":"How does ELMo work?","answer":"<p>ELMo utilizes a deep bidirectional language model with character-based word representations and bidirectional LSTMs. It processes sentences from both left to right and right to left, capturing the entire context of words. The resulting contextualized embeddings are used for downstream NLP tasks, enhancing their performance significantly.<\/p>"},{"question":"What are the key features of ELMo?","answer":"<p>ELMo's key features include context sensitivity, polysemy handling, out-of-vocabulary (OOV) support, transfer learning, and state-of-the-art performance on NLP tasks. Its contextual embeddings enable more accurate word representations based on sentence context, making it highly versatile and effective.<\/p>"},{"question":"What types of ELMo models exist?","answer":"<p>There are two main types of ELMo models:<\/p><ol><li><p>Original ELMo: This model generates context-sensitive word embeddings based on bidirectional LSTMs, providing word representations based on the entire sentence context.<\/p><\/li><li><p>ELMo 2.0: Building upon the original ELMo, this model incorporates self-attention mechanisms in addition to bidirectional LSTMs, further refining contextual embeddings for improved performance.<\/p><\/li><\/ol>"},{"question":"How can ELMo be used?","answer":"<p>ELMo finds applications in various NLP tasks such as sentiment analysis, named entity recognition, question answering, and machine translation. Its context-aware word representations enhance the performance of these tasks by capturing nuanced meanings and emotions.<\/p>"},{"question":"What challenges are associated with using ELMo?","answer":"<p>Using ELMo may present challenges such as high computational cost, long inference time, and integration complexity. However, researchers have explored optimization techniques, model distillation, and hardware acceleration to mitigate these issues.<\/p>"},{"question":"What are the future perspectives for ELMo?","answer":"<p>The future of ELMo holds promising advancements, including efficiency improvements, multilingual support, continual learning, and model compression. These developments will further enhance ELMo's capabilities and accessibility in the evolving field of NLP.<\/p>"},{"question":"How can proxy servers benefit from ELMo?","answer":"<p>Proxy servers can benefit from ELMo through enhanced content filtering, language-aware routing, anomaly detection, and multilingual proxying. ELMo's contextual embeddings enable better identification of inappropriate content and improved user experience.<\/p>"},{"question":"Where can I find more information about ELMo?","answer":"<p>For more information about ELMo and its applications, you can refer to the following resources:<\/p><ol><li>ELMo: Embeddings from Language Models (<a href=\"https:\/\/allennlp.org\/elmo\" target=\"_new\">https:\/\/allennlp.org\/elmo<\/a>)<\/li><li>Original ELMo paper (<a href=\"https:\/\/www.aclweb.org\/anthology\/N18-1202.pdf\" target=\"_new\">https:\/\/www.aclweb.org\/anthology\/N18-1202.pdf<\/a>)<\/li><li>ELMo 2.0: Missing Pretraining (<a href=\"https:\/\/www.aclweb.org\/anthology\/P19-1613.pdf\" target=\"_new\">https:\/\/www.aclweb.org\/anthology\/P19-1613.pdf<\/a>)<\/li><li>Tutorial on ELMo by AI2 (<a href=\"https:\/\/github.com\/allenai\/allennlp\/blob\/main\/tutorials\/how_to\/elmo.md\" target=\"_new\">https:\/\/github.com\/allenai\/allennlp\/blob\/main\/tutorials\/how_to\/elmo.md<\/a>)<\/li><\/ol>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477061","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477061\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468299"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=477061"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}