100,000 元总奖金丨“未来杯 AI 挑战赛” baseline正式发布-技术圈

算法能否判断一篇冷门角落里的论文，比一篇在自媒体上刷屏的论文更适合你读？

与这个问题息息相关，今年的“未来杯 AI 挑战赛” 赛题设置为预测一篇论文对哪些学者具有吸引力。日前，这一由智谱・AI 与 AI TIME 联合承办的比赛已正式启动（比赛页面可以访问“阅读原文”）。

本次比赛仍在进行中，并总奖金池 100000 元，使用 AMiner提供的数据集，任务、数据集、解题思路详解见“数据实战派”文章《10万元奖金：2021未来杯人工智能科技探索大赛赛题详解》。在此基础之上，本文是针对该竞赛的baseline介绍。

baseline地址：
https://fs.163.com/fs/display/?file=uC5xvCqblbWSGR0PpITsZx8lDrLyAiNgbAEpnVKGwqRlugVbRQA-vZO5H4uvsTi23KynHa_UhDxa_aE1_KSbGQ

一、数据处理

1. train/valid/test数据集划分：

将给定的train data按照train/valid/test划分，进行offline的training和test。

划分的原则：我们构建的是paper-expert pairs，希望借助这样的样本对来分别学习paper的embedding和expert的embedding，最后根据竞赛的要求，进行top-K的召回，为每一篇paper召回500个相关experts。因此，划分原则是对每一篇paper，与该paper交互的所有的experts，随机选择一个作为test，再选择一个作为valid，其余的experts作为train。这里，当该paper交互的experts少于3人时，我们将该paper和其交互的experts全部作为train data。

2. 数据预处理

如上述数据集的划分所言，我们的训练样本是paper-expert pairs。现在，我们需要对每对paper-expert进行预处理，下面分别进行预处理介绍：

Paper：在给定的数据集中，每一篇paper有多种属性，包括id、title、abstract、keywords等等（中文和英文版本），这里，我们选择title、abstract、keywords（中文版）这三个属性来作为paper的feature进行数据预处理。对这些feature的处理需要用nlp领域的知识。这里我们选择的是oag-bert来将paper的这些属性作为feature输入。值得注意的是输入到oag-bert的一段sequence，而这里的keywords是list，我们需要将其处理成seq，具体的做法是将keywords逐次相连成一个seq，每个keyword之间用空格隔开。title和abstract不用多做处理。这里我们给出了我们处理paperdata的代码，仅供参考。

def get_papers(self, data_dict): paper_infos = {} for iid in data_dict:paper_id, title, abstract, keywords, year = iid["id"]( iid["title"], iid["abstract"], iid[“keywords"], iid["year"#  process keywords str.keywords = "" if keywords !=for word in keywords:if word == keywords[0]: str_keywords = word else:str_keywords = str_keywords + 1 ' + word#  check data — remove unexist title and abstract data if title == "" and abstract =="" and str.keywords ==print("unexisting title and abstract and keywords paper data....") else:infos = {"title": title, "abstract": abstract, "keywords": str_keywords, "year": year} if paper_id in paper_infos:printC'repeat paper id  ")else:paper_infos[paper_id] = infos return paper_infos

Expert：在给定的数据集中，每个expert有多种属性，包括id、interests、tags、pub_info等等。这里我们选择interests作为每个expert的研究兴趣，若是interests为空，选择tags为研究兴趣，或者tags也为空，则interests为空。此外，我们还选择了pub_info作为feature，pub_info中包含了该expert发表的论文，这里我们同样选择title、abstract及keywords作为feature。expert数据的预处理代码：

def get_experts(self, data.dict): expert.infos = {} for expert in data.dict:expert.id = expert['id'] pub.info = expert["pub_info“] if expert.getC'interests", None) != None: interests = expert[“interests" ] elif expert.getC'tags", None) != None: tags = expertt "tags"] interests = tags else:interests = []#  process interests str_interests = '"' len_interests = len(interests) if len_interests != 0:for interest in interests:if interest == interests[9]:str.interests = interest['t' ] else:str.interests = str.interests + ' ' + interest!'t' ]#  process pub_info pub_infos - {}for iid in pub_info: pid = iidC'id"]if iid.get('title', None) 1= None: title = iid['title']else:title = '"'if iid.getf'abstract", None) != None: abstract = iid["abstract"] else:abstract = ""if iid.get("keywords". None) 1= None: keywords = iid[ "keywords'1] else:keywords = []infos = {"title": title, “abstract": abstract, "keywords": keywords} if pid in pub_infos:pass else:if title == "" and abstract == "" and keywords == []: print C'pub.info  ", iid)passelse:pub_infos[pidJ = infosU check expert data remove data unexist interests and pub.info if str_interests == "" and pub_infos == {}: print("unexisting_id", expert_id)else:if expert.id in expert.infos:passelse:info = {“interests": str.interests, “pub.info": pub.infos} expert_infos[expert_id] = inforeturn expert_infos

二、模型选择

这里，我们选择oag-bert作为我们学习embedding的模型。

oag-bert以每篇paper的title、keywords、abstract作为输入，来得到每一篇paper的embedding表示。

这里，我们需要先得到每篇paper的title、keywords、abstract等的token表示。具体的我们在batch数据的得到中详细讲述。

三、batch数据

这里，我们需要得到batch数据，进行oag-bert的finetune训练。值得注意的是，给定的数据集中只包含有paper-expert正的交互项，没有负样本，这里我们对每一个正paper-expert选择negs_num个负样本，具体的操作如下：将该paper没有交互过的所有experts作为负样本的候选集合，然后随机采样negs_num个。这样我们可以得到batch的数据，包括正、负样本。代码如下，注意，这里得到的训练样本集合的是[一篇paper, 一个正expert，negs_num个负experts]：

def generate_batch_data(self, paper.infos, experts.infos, batch, neg_num): batch_infos_anchor = [] batch_infos_pos = [] batch_infos_neg = []# generate anchor, pos, neg for pid, eid in batch:p.infos = paper.infostpid] e.infos = experts_infos[eid]#  build anchor, pos, neg#  anchoranchor.infos = <} if pid in anchor_infos:print("repeat pid  ")else:anchor_infos[pid] = p_infos batch_infos_anchor.append7anchor.infos)#  pospos.infos = {> if pid in pos.infos:print("repeat pid in pos.infos...") else:pos.infos[pid] = e.infos batch.infos.pos.append(pos.infos)#  negs#  random.sample K negsnegs.id * random.sampledist(set(experts.infos.keys()) - set(self.train_data_dict[pid])), neg.num) negs.infos = {} for neg in negs.id:if neg in negs.infos:print("repeat negs in negs.info  ")else:negs_infos[neg] = experts.infostneg]if experts.infostneg]["interests"] == "" and experts.infostneg]["pub.info"] == {>: printC'experts.empty", experts.infostneg])while True:if experts.infostneg]["interests"] != "": negs.infostneg] = experts.infostneg] break#  check pub.info when interests == "" elif experts_infos[neg]t"pub_info"] != {>:negs.infostneg] = experts.infostneg]|  break#  unexisting interests and pub.info (the information for this expert is useless) else:# sample another negprint("experts.infostneg]", experts.infostneg]) printC'expert.id", neg)printC'candidate.negs", len(list(set(experts_infos.keys()) - set(self.train_data_dict[pid]) - set(negs.id)))) neg = random.sample(list(set(experts_infos.keys()) - set(self.train_data_dict[pid]) - set(negs.id)), 1)[0] printC'neg.another", neg)batch.infos.neg.append(negs.infos)return batch.infos.anchor, batch.infos.pos, batch.infos.neg

上述得到的batch数据并不是可以直接输入到oag-bert中的数据，我们需要将这些seq数据词化，也就是得到这些seq的tokens。下面给出得到token的代码：

def get_batch_tokens(self, infos, flag): batch_tokens = [] for info in infos: tokens.dict = {>for p_id, p_info in info.items():# get tokenstokens = self.build_bert_inputs(p_info, flag) #print("tokens...", tokens) if p_id in tokens_dict:printC'repeat p_id  '')else:tokens_dict[p_id] = tokens batch_tokens.append(tokens_dict) return batch.tokensdef build_bert_inputs(self, p_info, flag): if flag == “anchor":#  title & abstract & keywordsif p_info.get("abstract". None) != None: abstract = p_info[”abstract"] else:abstract =if p_info.get(“title", None) != None: title = p.infoC'title"]else:title = " "if p_info.get("keywords", None) != None: keywords = p_info[“keywords"]else:keywords = ""return self.oagbert.build_inputs(title=title, abstract=abstract, concepts=keywords) elif flag == "pos" or flag == "neg":#  expertsif p_info.get("interests", None) != None: interests = p_info["interests"] else:interests = ""e_tokens = self.oagbert.build_inputs(title=interests, abstracts''")#  prcoess experts'pub infos expert_pub_infos = p_info["pub_info“] pub.tokens = {>for pid, expert_pub_info in expert_pub_infos.items(): title = expert_pub_info['title'] abstract = expert_pub_info['abstract'] keywords.list = expert_pub_info['keywords'] keywords = '"'if len(keywords_list) != 0: for word in keywords.list:if word == keywords_list[9]: keywords = wordelse:keywords = keywords + ' ' + wordp_tokens = self.oagbert.build_inputs(title=title, abstract=abstract, concepts=keywords) if pid in pub_tokens:printC'repeat pid  ")else:pub_tokens[pid] = p_tokenstokens = {"interests": e.tokens, "pub.info": pub.tokens} return tokens else:raise Exception(“undefine flag")return

四、得到paper embedding

当得到paper的tokens后，我们可以将其输入到oag-bert，得到paper的embedding。这里，我们简单讲解一下：

当我们调用get_batch_tokens()函数，会得到paper的tokens，这里的tokens包括input_ids、input_masks、token_type_ids、masked_lm_labels、position_ids、position_ids_second、masked_positions、num_spans。我们的oag-bert的输入需要input_ids、input_masks、token_type_ids、position_ids、position_ids_second作为输入，输出paper的embedding表示pooled_output：

input_ids, input.masks, token_type_ids, masked_lm_labels, position_idsf position_ids_second, maksed_positions, num_spans = token pooled_output = model.bert.forwardC input_ids=torch. Long Tensor (input_ids) .unsqueezed(0) , cuda (), token_type_ids=torch.LongTensor(token_type_ids).unsqueeze(0).cuda(),attention_mask=torch.LongTensor(input_masks).unsqueeze(0).cuda(), output_all_encoded_layers=False, checkpoint_activations=False,position_ids=torch.LongTensor(position_ids).unsqueeze(0).cuda(), position_ids_second=torch,LongTensorf position_ids_second).unsqueeze(0).cuda())

五、得到expert embedding

当得到expert的tokens后，我们可以将其输入到oag-bert，得到expert的embedding。值得注意的是，这里expert的embedding由两部分组成：

1. expert的interests的embedding；

2. expert的pub_info的embedding。

首先，我们先来看interests的embedding。

当我们调用get_batch_tokens()函数，会得到expert的interests的tokens，这里的tokens同样包括input_ids、input_masks、token_type_ids、masked_lm_labels、position_ids、position_ids_second、masked_positions、num_spans。我们的oag-bert的输入需要input_ids、input_masks、token_type_ids、position_ids、position_ids_second作为输入，输出expert的interests的embedding表示pooled_output_expert_interests，注意若是该expert的interests为空，这时interests的embedding为None：

# interests process interests = token[“interests“]#print(“interests", interests)input.ids, input_masks, token_type_ids, masked_lm_labels, position_ids, position_ids_second, maksed_positions, num_spans = interests if input_ids != []:_，pooled_output_expert_interests = model.bert.forwardt input_ids=torch.LongTensor(input_ids).unsqueeze(0).cuda(), token_type_ids=torch.LongTensor(token_type_ids).unsqueeze*0) .cudaO, attention_mask=torch.LongTensor(input_masks).unsqueeze(0).cudat), output_all_encoded_layers=False, checkpoint_activations=False,position_ids=torch. LongTensor(position_ids) .unsqueezed(0).cuda(), position_ids_second=torch.LongTensor(position_ids_second).unsqueeze(0).cuda())else:pooled_output_expert_interests = None

下面，我们接着看pub_info的embedding，本质上而言，pub_info就是对experts发表的每一篇paper做embedding表示，和paper的embedding得到的方式相同：

# pub_info of experts process pub.info = token["pub_info"] pub_info_embed = []for pid, p_token in pub_info.items():input_ids, input_masks, token_type_ids, masked_lm_labels, position_ids, position_ids_second, if input_ids != []:_,pooled_output_expert_pub = model.bert.forward( input_ids=torch.LongTensor(input_ids).unsqueezed) .cuda(), token_type_ids=torch.LongTensor(token_type_ids).unsqueeze(0).cuda(), attention_mask=torch.LongTensor(input_masks).unsqueeze(0),cuda(), output_all_encoded_layers=False, checkpoint_activations=False,position_ids=torch.LongTensor(position_ids).unsqueeze(0).cuda(), position_ids_second=torch.LongTensor(position_ids_second).unsqueeze(0).cuda()) pub.info.embed.append(pooled_output_expert_pub)

值得注意的是，每个expert发表的paper的数量不同，我们需要利用该expert发表的所有paper。

最后，我们结合interests embedding和pub_info embedding。这里，我们简单的使用了torch.mean()来进行merge这两种embedding来得到最终的expert embedding。

#  merge interests_embed and pub_info_embed#  here, we use torch.meanO for merge#  check  if pooled_output_expert_interests == None: if len(pub_info_embed) != 0:pooled_output_expert_cat = torch.cat(pub_info_embed)else:pooled_output_expert_cat = Noneelse:if len(pub_info_embed) != 0:pooled_output_expert_cat = torch.cat((pooled_output_expert_interests, torch.cat(pub_info_embed)), 0)else:pooled_output_expert_cat = pooled_output_expert_interests pooled_output_expert_final = torch.mean(pooled_output_expert_cat, 0).view(l, configC1output_dim'])

各位选手可以对这步merge过程进行改进，以获得更好的expert的embedding表示。

六、loss及训练（finetune）

当得到embedding后，我们需要定义我们训练所需的loss。这里我们采用的是infonce loss。具体的loss形式如下：

关于infonce loss的详细理解，各位选手可以自行学习，这里不再赘述。

# infoNCE lossclass infoNCE(nn.Module):def   init  (self):super(infoNCE, self).__init__()self.T = 0.07self.cross_entropy = nn.CrossEntropyLoss().cuda()def forward(self, query, pos, neg):query = F.normalize(query.view(batch_size, 1, dim) p=2,dim=2 )pos = F.normalize(pos.view(batch_size, 1, dim), p=2,dim=2 )neg = F.normalize(neg.view(batch_size, K, dim), p=2,dim=2 )pos_score = torch.bmm(query, pos.transposed(1, 2)) #B*1*1neg_score = torch.bmm(query, neg.transposed(1, 2)) #B*1*K# logits:B*(K+l)logits = torch.cat([pos_score, neg_score], dim=2). squeeze()logits /= self.T labels = torch.zeros(logits.shape[0], dtype=torch.long).cuda() info_loss = self.cross_entropy(logits, labels)return info_loss

注意：我们加了MLP去非线性变换了oag-bert得到的embedding，以便更好地finetune。

class MLP(nn.Module):def   init  (self, in_dim):super(MLP, self).  init  ()self.projection = nn.Sequential( nn.Linear(in_dim, in_dim), nn.ReLU(),nn.Linear(in_dim, in_dim)def forward(self, x):x = self.projection(x)return x

整个的training过程的代码如下：

#  infoNCE losscriterion = infoNCE)).cuda))optimizer = torch.optim.Adam([)'params':model.parameters))>,{’params': projection.parameters))}], lr=config["learning_rate“])model.train)) projection.train)) best.mrr = -1 patience = 0#  finetuningfor epoch in range(epochs):random.shuffle(train_batches) batch_loss = [] batch_num = 0for batch in train.batches: batch.num += 1#  get anchor pos neganchor, pos, neg = data_loader.generate_batch_data(paper_infos, experts.infos, batch, config[“neg_num"])anchor.tokens = data_loader.get_batch_tokens(anchor, "anchor") pos.tokens = data_loader.get_batch_tokens(pos, "pos") neg.tokens = data_loader.get_batch_tokens(neg, "neg")anchor_emb = get_batch_embed(anchor_tokens, "anchor") pos_emb = get_batch_embed(pos_tokens, “pos") neg_emb = get_batch_embed(neg_tokens, “neg")#  add MLP#  infoNCE lossloss = criterion)projection(anchor_emb), projection(pos_emb), projection(neg_emb)) print)"loss...", loss.item)))#  compute gradient and do Adam step optimizer.zero.grad))loss.backward)) optimizer.step))if batch.num > 1 and batch_num % valid.step == 0:print) "evalute  ")t.valid = time))mrr = evaluatefmodel, valid.batches, data.loader, paper_infos, experts_infos) print)"time for valid  ", time)) - t_valid)print)"Epoch:{} batch:{} loss:{} mrr:{}".format(epoch, batch_num, loss.item)), mrr)) if mrr > best_mrr: best_mrr = mrr tt save modeltorch.save(model.state_dict(), output.dir + "oagbert")print)“Best Epoch:{} batch:{} loss:{} mrr:)}".format(epoch, batch.num, loss.item)), mrr))else:patience += 1if patience > config[“patience"]:printC("Best Epoch:{} batch:)} loss:)} mrr:)}".format(epoch, batch.num, loss.item)), mrr))model.train() projection.train()

七、Test/valid

最后，我们在valid/test上进行验证/测试，valid可以用来调整我们模型的超参，test来测试我们模型的泛化能力。这些都是offline的测试。最终，保存效果最好的模型参数，再进行online的valid和test。

这里，我们用mrr来作为评价指标，当然，各位选手可以选择给定的评价指标。

注意：由于test时，为每篇paper做全局召回，耗时太大，我们采用的是随机sample 100个负样本，与test/valid中的正例进行rank。

为了进一步节省test的时间，我们采用了两种test方式：

1. 每个batch共享100个negs。

2. 整个test共享100个negs。

def evaluate(model, valid_batches, data_loader, paper_infos, experts_infos): model.eval() mrr =0.0 total_count = 0 with torch.no_grad():"""negs = data_loader.generate_negs_data(paper_infos, experts_infos, configt"Negs"]) neg_tokens = data_loader.get_batch_tokens(negs, "neg") neg_emb_candidates = get_batch_embed(neg_tokens, "neg")"""share batch negs"""for batch in valid_batches:anchor, pos, _ = data_loader.generate_batch_data_test(paper_infos, experts_infos, batch, configt"Negs"])#  use too much timeanchor_tokens = data_loader.get_batch_tokens(anchor, "anchor") pos_tokens = data_loader.get_batch_tokens(pos, "pos")#  use too much timeanchor_emb = get_batch_embed(anchor_tokens, "anchor") pos_emb = get_batch_embed(pos_tokens, "pos") neg_emb = neg_emb_candidates.repeat(len(batch), 1)#  batch share negs#for batch in valid_batches:#  anchor, pos, negs = data_loader.generate_batch_data_test(paper_infos, experts_infos, batch, configt"Negs"])#  # use too much time#  tt = timeO#  anchor_tokens = data_loader.get_batch_tokens(anchor, "anchor")#  print ("time...", timeO - tt)#  tt = timeO#  pos_tokens = data_loader.get_batch_tokens(pos, "pos")#  print ("time...", timeO - tt)#  tt = timeO#  neg_tokens = data_loader.get_batch_tokens(negs, "neg")#  print ("time...", timeO - tt)#  # use too much time#  tt = timeO#  anchor_emb = get_batch_embed(anchor_tokens, "anchor")#  print ("time...", timeO - tt)#  tt = timeO#  pos_emb = get_batch_embed(pos_tokens,  "pos")#  print ("time...", timeO - tt)#  tt = timeO#  neg_emb_candidates = get_batch_embed(neg_tokens, "neg")#  print ("time...", timeO - tt)#  neg_emb = neg_emb_candidates.repeat(len(batch), 1)#  anchor & pos_embedanchor_emb = F.normalize(anchor_emb.view(-l, 1, dim), p=2, dim=2)pos_emb = F.normalize(pos_emb.view(-l, 1, dim), p=2, dim=2)neg_emb = F.normalize(neg_emb.view(-l, configt"Negs"], dim), p=2, dim=2)pos_score = torch.bmm(anchor_emb, pos_emb.transpose(1, 2)) # B*l*l neg_score = torch.bmm(anchor_emb, neg_emb.transpose(1, 2))  # B*l*Negs#  logits:B*(l+Negs)logits = torch.cat([pos_score, neg_score], dim=2).squeeze() logits = logits.cpuO .numpyOfor i in range(batch_size): total_count += 1 logits_single = logits[i] rank = np.argsort(-logits_single) true_index = np.where(rank==0)[0][0] mrr += np.divide(1.0, true_index+l) mrr /= total_count return mrr

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