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## ざっくり言うと
- キーワード検索におけるランク学習と次のキーワードのsuggestionをmulti-taskで学習する
- 内部状態を共有してmulti-task学習することが両者の精度向上につながる
- 一連の検索の過程(session)をLSTMでencodingして,履歴を活かしたranking及びsuggestionを行なっている
- multi-task neural …
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hi
@rodrigonogueira4
The paper, "Multi-Stage Document Ranking with BERT", mentioned that `we use the [CLS] vector as input to a single layer neural network to obtain a probability of the candidate …
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Firstly thank you for putting together this awesome repo 🙌🏽. I think I speak for every user here, you guys have made benchmarking of IR so much easier that even folks new to the field can get started …
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Hi, since the model used for CNNDM is `Facebook/bart-large-cnn`, which means the model actually got fine-tuned on the CNNDM training set. Considering the Neural model's amazing capacity for memorizati…
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Since https://github.com/KichangKim/DeepDanbooru exists, and there are other networks like:
- https://github.com/BertMoons/Comparing-CNN-Architectures
- https://github.com/CeLuigi/models-comparison.…
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A common, modern approach to ranking is to perform multiple stages of reranking, with progressively more expensive but effective rerankers [1][2][3]. For example a BM25 first-stage will rank all docum…
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https://arxiv.org/pdf/1802.08988.pdf
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I have never seen a loss function which compare positive examples and negative examples. All the loss function I have seen so far (i'm still a student) compare the true value of _y_ with an estimated …
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## 一言でいうと
新型コロナの文献を検索できるシステムを開発した研究。LuceneベースのAnseriniをエンジンに使用し、BM25(TF-IDF+文書長が短い(=情報密度が高い)もの優先)で抽出した後にT5ベースで学習したモデル(クエリとドキュメントの関連度を直接出力する)でリランクしている。UIはRailsのBlacklightを使用。
### 論文リンク
https:/…
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