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        <h1 align="center">Code-switching Sentence Generation</h1>
        <h2 align="center">by Generative Adversarial Networks and its Application to Data Augmentation</h3>
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        <h3 align="center">Code-switching point (CSP) prediction</h3>
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        <h4 align="center">LectureSS</h4>
        <h5 align="center">(the same as Table. 4 in the article)</h5>
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                    <td align="center">Ground truth
                    </td>
                    <td>Causality 這個 也是 你 所 讀 過 的 就是 指 我 output at-any-time 只 depend-on input
                    </td>
                </tr>
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                    <td align="center">Input
                    </td>
                    <td>因果性 這個 也是 你 所 讀 過 的 就是 指 我 輸出 在任意時間 只 取決於 輸入 <br>
(Causality, this is also what you have read, that means what I output at any time only depends on input)
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                    <td align="center">Random
                    </td>
                    <td>因果性 this 也是 你 所 讀 過 的 就是 指 我 output 在任意時間 只 取決於 輸入
                    </td>
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                    <td align="center">Noun
                    </td>
                    <td>Causality 這個 也是 你 所 讀 過 的 就是 指 我 輸出 在任意時間 只 取決於 input
                    </td>
                </tr>
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                    <td align="center">Proposed
                    </td>
                    <td>Causality this also 你 所 read 過 的 就是 指 我 output 在任意時間 只 取決於 輸入
                    </td>
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                    <td align="center">Proposed (+pos)
                    </td>
                    <td>Causality 這個 也是 你 所 讀 過 的 就是 指 我 output at-any-time 只 depend-on 輸入
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            <details>
            <summary>
                <B>Description:</B>
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            <p>
                In this case, the code-switching points appear at nouns
                (causality, input), verbs (output, depend on) and an adverb (at any time).
                The rule-based approach "noun" is accurate, but cannot find out all CSPs.
                The proposed method with POS tagging can find out more CSPs.
            </p>
            </details>
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