\begin{table*}[h!]
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{1}{|l|}{Stage} & \multicolumn{1}{l|}{Dataset} & \multicolumn{1}{l|}{Max-epoch} & \multicolumn{1}{l|}{Early-stop} & \multicolumn{1}{l|}{Pooling method} & \multicolumn{1}{l|}{$\lambda$} \\ \hline
\multirow{3}{*}{1}          & WordNet                      & 140                            & 40                              & None                                & 0.0                         \\ \cline{2-6} 
                            & Oxford                       & 50                             & 10                              & None                                & 0.0                         \\ \cline{2-6} 
                            & Urban                        & 30                             & 5                               & None                                & 0.0                         \\ \hline
\multirow{3}{*}{2}          & WordNet                      & 70                             & 40                              & Max                                 & 0.8                         \\ \cline{2-6} 
                            & Oxford                       & 50                             & 10                              & Max                                 & 0.8                         \\ \cline{2-6} 
                            & Urban                        & 15                             & 5                               & Max                                 & 0.8                         \\ \hline
\end{tabular}
\caption{Detailed settings on each of our training stages, including max-epoch, early-stop threshold, pooling method and loss weight $\lambda$.}\label{tab-settings}
\end{table*}
\begin{equation}\label{task formulation}
P(D|W,C) = \prod \limits_{t=0}^T p(d_t|d_{<t},W,C)
\end{equation}

\begin{equation}\label{encoder}
\mathbf{H}_0={\rm Emb}({\rm Splice}(W, C))
\end{equation}

Finally, a softmax function is added upon a linear head to transform $\mathbf{G}^{L_D}$ into a prediction distribution matrix $\mathbf{V}\in \mathbb{R}^{|V|\times |D|}$. Here $|V|$ and $|D|$ stand for the vocabulary size and the length of the ground-truth definition, respectively. To optimize, a cross-entropy loss is applied to measure the discrepancy between the generated distribution and the ground-truth distribution. 

After encoding, we use a pooling function $\rm f()$ to aggregate the $\mathbf{H}_{target}$ and $\mathbf{G}^{L_D}$ respectively, and obtain {target word representation} $\mathbf{h}$ and {definition representation} $\mathbf{g}$ with the same length:

\begin{equation}\label{}
\mathbf{h} = {\rm f}(\mathbf{H}_{target})
\end{equation}

\begin{equation}\label{}
\mathbf{g} = {\rm f}(\mathbf{G}^{L_D})
\end{equation}

\begin{equation}\label{eq:contrastive}
L_C = \sum_{i=1}^N -log\frac{e^{{\rm sim}(h_i,g_i)}/\tau}{\sum_{j=1}^N e^{{\rm sim}(h_i,g_j)}/\tau}
\end{equation}

\begin{equation}\label{eq:Final_Loss}
L_{Final} = \lambda*L_C + (1-\lambda)*L_G
\end{equation}
{"value": "1", "type": "Other"} {"value": "140", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Max-epoch", "dataset": "WordNet"} {"value": "40", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Early-stop", "dataset": "WordNet"} {"value": "0.0", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "\\lambda", "dataset": "WordNet"} {"value": "50", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Max-epoch", "dataset": "Oxford"} {"value": "10", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Early-stop", "dataset": "Oxford"} {"value": "0.0", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "\\lambda", "dataset": "Oxford"} {"value": "30", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Max-epoch", "dataset": "Urban"} {"value": "5", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Early-stop", "dataset": "Urban"} {"value": "0.0", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "\\lambda", "dataset": "Urban"} {"value": "2", "type": "Other"} {"value": "70", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Max-epoch", "dataset": "WordNet"} {"value": "40", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Early-stop", "dataset": "WordNet"} {"value": "0.8", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "\\lambda", "dataset": "WordNet"} {"value": "50", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Max-epoch", "dataset": "Oxford"} {"value": "10", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Early-stop", "dataset": "Oxford"} {"value": "0.8", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "\\lambda", "dataset": "Oxford"} {"value": "15", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Max-epoch", "dataset": "Urban"} {"value": "5", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "Early-stop", "dataset": "Urban"} {"value": "0.8", "type": "Hyper-parameter/Architecture", "model": ["T5-Contrast", "Ours"], "parameter/architecture name": "\\lambda", "dataset": "Urban"}