fixed equation reference

model.tex

@@ -11,7 +11,7 @@ In the SICK dataset, textual entailment is a multi-class problem with three clas
  \end{aligned}
 \end{equation}
 
-We are given a training set of examples, where each example contains two sentences that we convert to two lists of tokens $N$ and $M$. We then seek to align each token in these lists to either: (1) a token in the other set or (2) a NULL token that we add to each list. The latter option is equivalent to deleting that token. We find the alignment assignment for each token by finding the values for a set of binary variables $\{ 1_{n,m} \}$ that indicate whether tokens $x_n$ and $x_m$ are aligned. We do this by solving equation~(\ref{eq:ilp}) where $\bw$ is our vector of parameters and $f$ is a function that returns the feature vector given two tokens.\footnote{Note that in equation~\ref{eq:ill} and equation~\ref{eq:learning} we assign the index of the NULL token to be 0 for both $N$ and $M$.}
+We are given a training set of examples, where each example contains two sentences that we convert to two lists of tokens $N$ and $M$. We then seek to align each token in these lists to either: (1) a token in the other set or (2) a NULL token that we add to each list. The latter option is equivalent to deleting that token. We find the alignment assignment for each token by finding the values for a set of binary variables $\{ 1_{n,m} \}$ that indicate whether tokens $x_n$ and $x_m$ are aligned. We do this by solving equation~(\ref{eq:ilp}) where $\bw$ is our vector of parameters and $f$ is a function that returns the feature vector given two tokens.\footnote{Note that in equation~\ref{eq:ilp} and equation~\ref{eq:learning} we assign the index of the NULL token to be 0 for both $N$ and $M$.}
 
 %\begin{multline} \label{eq:learning}
 %\underset{\{ 1_{n,m} \}}{\text{max}}  \bw^T \sum_n^{\|N\|+1} \sum_m^{\|M\|+1} 1_{n,m}\frac{f(x_n, x_m, N, M)}{\|N\| + \|M\|} \\