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Figure 6: Visualization of word pairs with the male-female relationship.

Figure 6 Visualization of word pairs with the male-female relationship.

Related Figures (7)

Figure 1: Skip-Gram model.
Figure 1: Skip-Gram model.
Figure 2: BERT model.
Figure 2: BERT model.
Figure 3: Our proposed model. R”*¢* and values V € R”*?* with multi-head attention transformation: ing context words Wj_ws, --, Wi-1, Wit1, +» Witws Into embeddings by using a target lookup table V and a context lookup table V’, respectively. The center word and context words are projected into v»,; and v,,,,,1 < [Jj] < ws, respectively, as Figure | shows. Given a training corpus C = {8. = wi, W2,-.-, Wn, }|AL1, The training objective is to minimizes the loss function:
Figure 3: Our proposed model. R”*¢* and values V € R”*?* with multi-head attention transformation: ing context words Wj_ws, --, Wi-1, Wit1, +» Witws Into embeddings by using a target lookup table V and a context lookup table V’, respectively. The center word and context words are projected into v»,; and v,,,,,1 < [Jj] < ws, respectively, as Figure | shows. Given a training corpus C = {8. = wi, W2,-.-, Wn, }|AL1, The training objective is to minimizes the loss function:
Figure 4: Comparison between different window sizes.
Figure 4: Comparison between different window sizes.
Figure 5: Comparison between with/without attention.
Figure 5: Comparison between with/without attention.
Table 3: Evaluation on POS tagging and chunking. Table 2: Evaluation on word similarity and analogy.
Table 3: Evaluation on POS tagging and chunking. Table 2: Evaluation on word similarity and analogy.
Figure 7: Attention visualization of sentence “football is the most popular sport in Brazil”.
Figure 7: Attention visualization of sentence “football is the most popular sport in Brazil”.
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Computer ScienceSemantics (Computer Science)
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