\section{Participating Systems}
\label{sec:systems}

\begin{sidewaystable*}
\centering
\resizebox{0.85\textwidth}{!}{%

\begin{tabularx}{\textheight}{l|l|p{2cm}|p{8cm}|p{2.3cm}|p{2.75cm}|p{2.75cm}}
        
        \toprule
        \textbf{Track} & \textbf{Team} & \textbf{Baselines} &  \textbf{Framework} & \textbf{Markable ID} & \textbf{Train. Data} & \textbf{Dev. Data} \\
        \midrule
        \multirow{4}{2cm}{\textbf{Anaphora Resolution}} & UTD\_NLP & \citet{xu2020revealing} & Pipeline of mention detection and entity coreference components. Modifies baseline to handle singleton clusters and enforce dialogue-specific constraints. & Adapted from \citet{xu2020revealing} & {\CODICRAC} & {\CODICRAC} \\
        \cmidrule{2-7}
        & KU\_NLP & - & \cite{cui2019hierarchically} with ELECTRA-large for mention detection. A pointer-network \citep{vinyals2015pointer} based model for resolution. & \citet{cui2019hierarchically} + ELECTRA-large & {\CODICRAC} & {\CODICRAC} \\
        \cmidrule{2-7}
        & DFKI & - & (1) Workspace Coreference System incrementally  clusters mentions using semantic similarity based on embeddings and lexical features; (2) Mention-to-Mention system pairs same entity mentions & SpaCy / BiLSTM-CRF & {\CODICRAC} & {\CODICRAC} \\ \cmidrule{2-7}
        & Emory & \citet{joshi2020spanbert} & Adapts the baseline to recognize singletons and encode speakers for all turns. & \citet{joshi2020spanbert} + Singleton Recognition & {\CODICRAC} + OntoNotes \cite{pradhan2012conllst} + BOLT \citep{li-etal-2016-large} & {\CODICRAC} \\ \midrule\midrule
        \multirow{3}{2cm}{\textbf{Bridging Resolution}} & UTD\_NLP & \citet{yu-etal-2020-bridging} & A multi-pass sieve approach which used the baseline as one of the sieves and consists of a set of learning-based sieves (trained using SVMs) to target same-head bridging links. & Multi-task learning and SVM-based sieves & {\CODICRAC} & {\CODICRAC} \\
        \cmidrule{2-7}
        & KU\_NLP & - & Solved as an MRC problem. Model takes query ("\textit{What is related of ENTITY?"}) and the conversation as input, and outputs the answer entity span. & Obtained as a part of the MRC system & {\CODICRAC} + QUOREF & {\CODICRAC} \\
        \cmidrule{2-7}
        & INRIA & \citet{joshi-etal-2019-bert} & Leverage baseline's architecture to find the correct bridging antecedent in the gold setting. & \citet{joshi-etal-2019-bert} & {\CODICRAC} & {\CODICRAC} \\\midrule\midrule
        \multirow{2}{2cm}{\textbf{Discourse Deixis Resolution}} & UTD\_NLP & \citet{xu2020revealing} & Builds upon the baseline by classifying each span into candidate anaphor, a candidate antecedent, or a non-mention. Only resolves candidate anaphors to candidate antecedents. & Obtained as part of joint mention detection and deixis resolution & {\CODICRAC} & {\CODICRAC} \\
        \cmidrule{2-7}
        
        & DFKI & - & A Siamese  Network to  detect discourse anaphor-antecedent pairs. & SpaCy & {\CODICRAC} & {\CODICRAC} \\ \bottomrule
    \end{tabularx}
}
\caption{Summary of the Participating Systems\label{tab:summary}}
\end{sidewaystable*}

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%     \begin{longtable}{l|l|p{3.5cm}|p{3.5cm}|p{3.5cm}}
%     % \begin{tabular}{l|l|p{3.5cm}|p{3.5cm}|p{3.5cm}}
        
%         \caption{Summary of Participating Systems\label{tab:summary}} \\
%         \toprule
%         & \textbf{Track} & \multicolumn{1}{c|}{\textbf{Anaphora Resolution}} &  \multicolumn{1}{c|}{\textbf{Bridging Resolution}} & \multicolumn{1}{c}{\textbf{Discourse Deixis}}\\
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%         \endfirsthead
        
%         \multicolumn{5}{c}{{{\bfseries \tablename\ \thetable{} -- continued from previous page}}} \\
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%         & \textbf{Track} & \multicolumn{1}{c|}{\textbf{Anaphora Resolution}} &  \multicolumn{1}{c|}{\textbf{Bridging Resolution}} & \multicolumn{1}{c}{\textbf{Discourse Deixis}}\\
%         \midrule
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%         \bottomrule
%         \endlastfoot
        
%         \multirow{5}{1.6cm}{UTD\_NLP} & Baselines & \citet{xu2020revealing} & \citet{yu-etal-2020-bridging} & \citet{xu2020revealing} \\
%         \cmidrule{2-5}
%         & Framework & Pipeline of mention detection and entity coreference components. Modifies baseline to handle singleton clusters and enforce dialogue-specific constraints. & A multi-pass sieve approach which used the baseline as one of the sieves and consists of a set of learning-based sieves (trained using SVMs) to target same-head bridging links. & Builds upon the baseline by classifying each span into candidate anaphor, a candidate antecedent, or a non-mention. Only resolves candidate anaphors to candidate antecedents.\\
%         \cmidrule{2-5}
%         & Markable ID & Adapted from \citet{xu2020revealing} & Multi-task learning and SVM-based sieves & Obtained as part of joint mention detection and deixis resolution\\
%         \cmidrule{2-5}
%         & Train. Data & {\CODICRAC} & {\CODICRAC} & {\CODICRAC}\\
%         \cmidrule{2-5}
%         & Dev. Data &  {\CODICRAC} & {\CODICRAC} & {\CODICRAC}\\
%         \midrule
%         \midrule
%         \multirow{5}{1.6cm}{KU\_NLP} & Baselines & - & - & DNP \\
%         \cmidrule{2-5}
%         & Framework & \cite{cui2019hierarchically} with ELECTRA-large for mention detection. A pointer-network \citep{vinyals2015pointer} based model for resolution. & Solved as an MRC problem. Model takes query ("\textit{What is related of ENTITY?"}) and the conversation as input, and outputs the answer entity span. & DNP\\
%         \cmidrule{2-5}
%         & Markable ID & \citet{cui2019hierarchically} + ELECTRA-large & - & DNP\\
%         \cmidrule{2-5}
%         & Train. Data & {\CODICRAC} & {\CODICRAC} + QUOREF & DNP\\
%         \cmidrule{2-5}
%         & Dev. Data &  {\CODICRAC} & {\CODICRAC} & DNP\\
%         \midrule
%         \midrule
%         \multirow{5}{1.6cm}{DFKI} & Baselines & - & DNP & - \\
%         \cmidrule{2-5}
%         & Framework & (1) Workspace Coreference System incrementally  clusters mentions using semantic similarity based on embeddings and lexical features; (2) Mention-to-Mention system pairs same entity mentions & DNP & A Siamese  Network to  detect discourse anaphor-antecedent pairs.\\
%         \cmidrule{2-5}
%         & Markable ID & SpaCy / BiLSTM-CRF & DNP & SpaCy\\
%         \cmidrule{2-5}
%         & Train. Data & {\CODICRAC} & DNP & {\CODICRAC}\\
%         \cmidrule{2-5}
%         & Dev. Data &  {\CODICRAC} & DNP & {\CODICRAC}\\
%         \midrule
%         \multirow{5}{1.6cm}{Emory} & Baselines & \citet{joshi2020spanbert} & DNP & DNP \\
%         \cmidrule{2-5}
%         & Framework & Adapts the baseline to recognize singletons and encode speakers for all turns & DNP & DNP \\
%         \cmidrule{2-5}
%         & Markable ID & \citet{joshi2020spanbert} + Singleton Recognition & DNP & DNP\\
%         \cmidrule{2-5}
%         & Train. Data & {\CODICRAC} + OntoNotes \cite{pradhan2012conllst} + BOLT \citep{li-etal-2016-large} & DNP & DNP\\
%         \cmidrule{2-5}
%         & Dev. Data &  {\CODICRAC} & DNP & DNP\\
%         \midrule
%         \midrule
%         \multirow{5}{1.6cm}{INRIA} & Baselines & DNP & \citet{joshi-etal-2019-bert} & DNP \\
%         \cmidrule{2-5}
%         & Framework & DNP & They leverage baseline's architecture to find the correct antecedent for gold setting. & DNP \\
%         \cmidrule{2-5}
%         & Markable ID & DNP & \citet{joshi-etal-2019-bert} & DNP \\
%         \cmidrule{2-5}
%         & Train. Data & DNP & {\CODICRAC} & DNP \\
%         \cmidrule{2-5}
%         & Dev. Data & DNP & {\CODICRAC} & DNP \\
%     \end{longtable}
%     % \end{tabular}
% \end{center}
% % \end{table*}

A total of 55 individual participants registered for the {\CODICRAC} shared task on CodaLab.\footnote{Participants were allowed to create teams.} Among them, five teams submitted results for Task 1, three submitted results for Task 2, and two submitted results for Task 3. Teams UTD\_NLP, KU\_NLP, DFKI\_TalkingRobots, Emory\_NLP, and INRIA submitted system description papers. We summarize their approaches below (and in Table~\ref{tab:summary}):

\paragraph{UTD\_NLP} participated in all three tasks. For identity anaphora, they deployed a pipeline architecture consisting of a mention detection component and an entity coreference component. The coreference component extends \citet{xu2020revealing}'s implementation of \citet{lee2018higher} by modifying the objective so that it can output singleton clusters, and enforces dialogue-specific constraints. They setup a similar architecture for discourse deixis. However, they slightly modified the objective function in \citet{xu2020revealing} by classifying each span as a candidate anaphor, a candidate 
% \vspace{14cm}
antecedent, or a non-mention in the mention detection stage, and resolving only candidate anaphors to candidate antecedents later. The team used a multi-pass sieve approach for bridging resolution to target same-head bridging links, with \citet{yu-etal-2020-bridging}'s model as one of the sieves. In the gold setting, they trained an additional anaphor detection model (adapted from \citet{yu-etal-2020-bridging}) to first identify the bridging anaphors from the gold markables before sending them to the sieves.

\begin{table*}[t]
\centering
\begin{tabular}{l|cccc|c}
\toprule
\textbf{Team} & \LIGHTS & \AMI   & \PERSUASIONS & \SWITCHBOARDS  & \textbf{Avg.} \\
\midrule
\multicolumn{6}{c}{\textbf{Eval AR}} \\
\midrule
Emory                   & \textbf{80.33 (1)} & \textbf{63.98 (1)} & \textbf{78.41 (1)}      & \textbf{74.49 (1)} & \textbf{74.3 (1)}\\
UTD\_NLP                & 79.56 (2)         & 57.38 (3)          & 77.50    (2)           & 72.64 (2)         & 71.8 (2)\\
KU\_NLP                 & 69.16  (3)          & 57.59 (2)         & 71.09  (3)              & 65.67  (3)         & 65.9 (3)\\
DFKI (1) & 64.99 (4)         & 43.93 (4)          & 59.93 (4)               & 53.55 (4)          & 55.6 (4)\\
SCIR\footnotemark                    & 55.92 (6)          & 39.46 (5)          & 52.25  (6)              & 51.63  (5)         & 49.8 (5)\\
\midrule
Baseline & 52.45 (7)          & 36.11 (6)          & 51.97 (7)              & 45.80 (7)          & 46.6 (6) \\
\midrule
DFKI (2) & 61.26 (5)          & 00.00 (7)           & 59.20 (5)               & 51.24 (6)          & 42.9 (7) \\
\bottomrule
\end{tabular}
\caption{Performance on Task 1 (Evaluation Phase) -- Identity Anaphora (CoNLL Avg. F1)}
\label{tab:task1}
\end{table*}

\begin{table}[t]
\resizebox{\linewidth}{!}{
\centering
\begin{tabular}{l|cccc|c}
\toprule
\textbf{Team} & \LIGHTS & \AMI   & \PERSUASIONS & \SWITCHBOARDS  & \textbf{Avg.}  \\
\midrule
\multicolumn{6}{c}{\textbf{Eval Br (Gold)}} \\
\midrule
UTD\_NLP      & \textbf{19.73} & \textbf{19.65} & \textbf{31.40}      & \textbf{21.10} & \textbf{23.0} \\
KU\_NLP       & 16.67          & 15.30          & 18.79               & 18.33          & 17.3          \\
INRIA         & 9.35           & 6.00           & 16.28               & 7.79           & 9.9           \\
\midrule
Baseline & 6.35 & 6.21 & 13.77 & 5.39 & 7.9\\
\midrule
\multicolumn{6}{c}{\textbf{Eval Br (Pred)}} \\
\midrule
UTD\_NLP      & \textbf{13.98} & \textbf{13.33} & \textbf{21.92}      & \textbf{15.26} & \textbf{16.1} \\
KU\_NLP       & 13.46          & 10.25          & 12.32               & 10.99          & 11.8  \\
\midrule
Baseline & 6.01 & 4.94 & 9.34 & 3.78 & 6.0\\
\bottomrule
\end{tabular}
}
\caption{Performance on Task 2 (Evaluation Phase) -- Bridging Anaphora (Entity F1)}
\label{tab:task2}
\end{table}

\begin{table}[t]
\resizebox{\linewidth}{!}{
\centering
\begin{tabular}{l|cccc|c}
\toprule
\textbf{Team} & \LIGHTS & \AMI   & \PERSUASIONS & \SWITCHBOARDS   & \textbf{Avg.}  \\
\midrule
\multicolumn{6}{c}{\textbf{Eval DD (Gold)}} \\
\midrule
UTD\_NLP            & \textbf{43.44} & \textbf{36.91} & \textbf{52.09} & \textbf{40.44} & \textbf{43.2} \\
\midrule

\multicolumn{6}{c}{\textbf{Eval DD (Pred)}} \\
\midrule
UTD\_NLP            & \textbf{42.70} & \textbf{35.35} & \textbf{39.64} & \textbf{35.43} & \textbf{38.3} \\
DFKI & 20.97          & 17.43          & 23.76          & 23.86          & 21.5  \\
\midrule
Baseline & 12.12 & 15.75 & 18.27 & 13.55 & 14.9\\
\bottomrule
\end{tabular}
}
\caption{Performance on Task 3 (Evaluation Phase) -- Discourse Deixis (CoNLL Avg. F1)}
\label{tab:task3}
\end{table}


\paragraph{KU\_NLP} submitted results for tasks 1 and 2. For identity anaphora, they leveraged \citet{cui2019hierarchically}'s model with an ELECTRA-large backbone for mention detection. The resulting mention representation, created from the constituent token representations, is then fed to a pointer-network \citep{vinyals2015pointer} based coreference resolution model for clustering. They solved the bridging resolution problem using a machine reading comprehension framework, where they constructed a query for each entity of the form -- \textit{"What is related of ENTITY?"}. The input of their model is the query and the document (i.e., all utterances of dialogue), and the output is the entity span that is the answer for the query. 

\paragraph{DFKI\_TalkingRobots (DFKI)} put forward two systems as their final submissions for the identity anaphora task. The Workspace  Coreference System (WCS) attempts to incrementally cluster mentions using semantic similarity based on embeddings combined with lexical feature heuristics,
whereas their Mention-to-Mention (M2M) architecture only focuses on mention pairs to make the decision. WCS and M2M use SpaCy and BiLSTM-CRF based mention detection components, respectively. For discourse deixis, the team deployed a Siamese Network to detect discourse anaphor-antecedent pairs. They only focused on demonstrative pronouns (as anaphors), which they detected using the SpaCy NLP pipeline. 

\paragraph{Emory\_NLP (Emory)} only participated in the identity anaphora task. Their system was adapted from the end-to-end  neural coreference resolution model of \citet{joshi2020spanbert}. They recognized singletons, encoded speakers for all turns, and leveraged other out-of-domain datasets during training.

\paragraph{INRIA} submitted an end-to-end transformer-based model fine-tuned for the bridging resolution task. They formulated the bridging problem as antecedent selection, and leveraged \citet{lee2018higher,joshi-etal-2019-bert}'s architecture to find the correct antecedent.

\footnotetext{Team SCIR did not submit a system description paper.}