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\title{ silp\_nlp at SemEval-2023 Task 2: Cross-lingual Knowledge Transfer for Mono-lingual Learning  }
% NOTE:
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\author{Sumit Singh \and  Uma Shanker Tiwary \\Indian Institute of Information Technology, Allahabad 
   \\ {\tt sumitrsch@gmail.com}  \\   {\tt ust@iiita.ac.in} \\}
% \author{Sumit singh \\   Indian Institute of Information Technology, Allahabad \\  
%   \texttt{sumitrsch@gmail.com} \\\And
%   Uma Shanker Tiwary \\ Indian Institute of Information Technology, Allahabad \\
%   \texttt{ust@iiita.ac.in} \\}

\begin{document}
\maketitle
\begin{abstract}
Our team silp\_nlp participated at SemEval-2023 Task 2- MultiCoNER \rom{2}. Our work made  systems for 11 mono-lingual tracks. For leveraging the advantage of all track knowledge we chose transformer based pretrained models, which have strong cross-lingual transferability. Hence our model trained in two stages, first stage for multi-lingual learning from all tracks and second for fine tuning individual tracks. Our work highlights that the knowledge of all tracks can be transferred to an individual track if the baseline language model has cross-lingual features. Our system positioned itself in top 10 for 4 tracks by scoring 0.7432 macro F1 score for Hindi track (7th rank) and 0.7322 macro F1 score for Bangla track (9th rank).
\end{abstract}


\section{Introduction}
\label{intro}


Multilingual Complex Named Entity Recognition)
\cite{multiconer2-report}  targets the recognition
of complex named entities in multilingual and
multi-domain texts, encouraging researchers to develop new approaches to extract diversified entity types. The semantic structure of data \cite{multiconer2-data}, which is also provided by organizers for training and validation and testing purpose, has six types of coarse grained entities: person, location, group, product, medical
and creative work with more than 30 fine grained entities of these 6 coarse grained entities. The dataset was provided for
12 mono-lingual tracks, and, in addition, this task also provided multi-language data encouraging the development of more generic and adaptive systems. NER task used in many other NLP tasks. Our team participated for 11 mono-lingual tracks ( Hindi,Bangla, English, German, Spanish, French, Italian, Portuguese, Swedish, Ukrainian, Chinese ). Our system scores 7th rank in Hindi and secure in top 10 in 4 languages \cite{multiconer2-report}.

Our system leveraged the advantage of cross-lingual learning through multi stage training similar to \cite{wang-etal-2022-damo} and \cite{he-etal-2022-infrrd}. For this in the first stage , we have selected Cross-lingual pretrained models which cover a group of languages of  our task and we fine tune these pretrained language models for a combined dataset of all tracks which languages are covered by these language models  for 10 epochs. It creates a model checkpoint which has knowledge of  multiple languages for our task. In second stage we fine tuned the model checkpoints which is  generated in first stage for each track individually.For Hindi and Bangla track our best model achieved by Muril large model \cite{https://doi.org/10.48550/arxiv.2103.10730} which is combinedly fine tuned for three tracks (English, Bangla, Hindi) in first stage and thereafter the model checkpoints fine tuned for Hindi and Bangla. For the best model for all other languages we fine tuned xlm-r \cite{conneau2020unsupervised} for all the tracks combinedly in the first stage and in the second stage fine tuned all mono-lingual tracks. 


In addition to multilinguality, there were two key challenges in this year's task,  fine-grained entity taxonomy with over 30 different classes, and noisy testing data which is generated by adding simulated errors  to the test set. Our best model for prediction available here\footnote{https://huggingface.co/sumitrsch}.





\section{Related Work}
\begin{figure*}
\centering
 \includegraphics[width=\linewidth,height=250pt]{Model.drawio (8).png}
  \caption{General Architecture of our system}
  \label{fig:mainmodel}
\end{figure*}

Transformer based language models like Bert \cite{devlin2019bert} shows competative result for several NLP task like NER but the problem of low-context situations, long-tail entities, emerging entities and complex entity outlined by the \citet{meng2021gemnet} hard to handle by these models since these models generate contextual word embeddings and pretrained on existing data only.
The problem of emerging entities for Web queriesin multi-Lingual Code-Mixed context addressed by \cite{fetahu2021gazetteer}. These challenges also occur in \cite{multiconer-report} and \cite{multiconer2-report}. \cite{fetahu2021gazetteer} and \citet{meng2021gemnet} integrated a gazetteer with  language models as a solution for these challenges. There are some challenges with the use of gazetteer, one of them is gazetteer should cover the entities in sufficient ratio and also for new domain and entity types a specific gazetteer is required, which is subject to availability of gazetteer. Another one challenge described in \citet{meng2021gemnet} is weak learning of the model due to the model knowing the entity type in advance for the entities, which are covered by gazetteer. 
  \\
 Our work is continuation of the multilingual NER task started in 2022 \cite{multiconer-report}, which has only 6 entities present for 11 mono-lingual track with multi-language and code-mixed track. The Challenge of \cite{multiconer-report}  was to predict complex entities like Creative works with low context for multi-lingual track. 
One of the leading approache to generate system for \cite{multiconer-report}
the problem is based on a Knowledge-based System \cite{wang-etal-2022-damo} for lower context examples by augamentation of text from Wikipedia.
\cite{chen-etal-2022-ustc} also performed well for all tracks by integrating language models like BERT (Devlin et al., 2019) with a gazetteer network by minimizing the KL divergence. 
 \cite{he-etal-2022-infrrd} and \cite{wang-etal-2022-damo} apply multi-stage training for leveraging the advantage of multi-lingual tracks. \cite{he-etal-2022-infrrd} also used Ensemble architecture and data augmentation for making a more robust model for noisy environments. 
 \cite{singh-etal-2022-silpa} fine tuned muril-large and achieve 0.69 and 0.59 F1-Scores for Hindi and Bangla tracks.


\section{Data}
\begin{table}[h]
\begin{center}

\begin{tabular} {| p {1.8 cm} | p {1.1 cm} | p {1.1 cm} | p {1.2 cm} |}
    \hline
    
    \textbf{Language} & \textbf{Train Size} & \textbf{Dev Size} & \textbf{Test Size} \\ \hline
    
     Bangla & 9708 & 507 & 19859 \\ \hline    
     German & 9785 & 512 & 20145 \\ \hline
     English & 16778 & 871 & 249980 \\ \hline
     Spanish & 16453 & 854 & 246900 \\ \hline
     French & 16548 & 857 & 249786 \\ \hline
     Hindi & 9632 & 514 & 18399\\ \hline
     Italian & 16579 & 858 & 247881 \\ \hline
     Portuguese & 16469 & 854 & 229490\\ \hline
     Swedish & 16363 & 856 & 231190\\ \hline
     Ukrainian & 16429 & 851 & 238296\\ \hline
     Chinese & 9759 & 506 & 20265\\ \hline
     Farsi & 16321 & 855 & 219168\\ \hline
     Multi & 170823 & 8894 & 358667\\ \hline
% \\
% \\
\end{tabular}
\caption {Data set distribution} \label{tab:datasetdistribution}
\end{center}
\end{table}
\begin{table*}[h]
\begin{center}
\begin{tabular} {| P {6 cm} | P {4 cm} | P {4 cm} |}
    \hline
    \textbf{Hyper parameters} & \textbf{ Muril setup}  & \textbf{ xlm-r setup} \\ \hline
Baseline language model for first stage & google/muril-large-cased & xlm-roberta-large\\ \hline    
Hidden size for language model & 1024 & 1024 \\ \hline
Classification layer & Linear layer with cross-entrophy loss function & Linear layer with cross-entrophy loss function \\ \hline
Learning rate for language models & 5e-06 & 1e-06\\ \hline

Learning rate for the classification layer & 5e-06 & 1e-06\\ \hline
First-stage training epochs & 10 & 10 \\ \hline
Second-stage training epochs & 20 & 20 \\ \hline
Batch size & 64 & 64\\ \hline  
Dropout rate & 0.1 & 0.1\\ \hline
% $\alpha$  for the second stage training & 5 (Softmax and Span), 100 for CRF  \\ \hline
Activation function & Relu & Relu\\ \hline
Optimizer & AdamW & AdamW \\ \hline

% \\
\end{tabular}

\caption {F1 score(\%) for Muril and xlm-r setup} \label{tab:hyperparameter}
\end{center}
\end{table*}
This shared task provide  11 mono-lingual track and 1 multi-lingual track 
  \cite{multiconer2-data} to encourages NLP researchers to
develop NER systems. All tracks have splitted training, development and testing data however testing data provided after final ranking is done.
Size of training, development and testing data of all tracks are tabulated in Table \ref{tab:datasetdistribution}.



Along with complex entities
like creative works there are 6 coarse grained entities and more than 30 fine grained entities are there for all tracks, which are really challenging as they are
harder to recognize. Organizers \cite{multiconer2-data} provided   around  9700 sentences only for training for Hindi, Bangla, Chinese and German tracks, this made these tracks more challenging than other tracks as for other mono-lingual tracks around 16500 training data were available.

\section{Methodology}
All tracks data are divided  into training and validation data by the task organizer.
Similar to  \cite{he-etal-2022-infrrd} and \cite{wang-etal-2022-damo} our system selected strong cross-lingual transfer models ( XLM-R \cite{conneau2020unsupervised}, pre-trained on more than 100 languages and  Muril \cite{https://doi.org/10.48550/arxiv.2103.10730}, pre-trained on all Indic and English languages ) for two stage training as base model. In the first stage we performed multi-lingual training in which training data of all tracks were utilized into selected language models. Figure \ref{fig:mainmodel} showed that in first stage our model  utilized
the annotations of all the tracks. This experiment was done with different hyper parameters for 10 epochs and the best multi-lingual checkpoint was selected based on average loss on validation data. Thereafter in second stage we use the fine-tuned multilingual-checkpoint of the first stage as the initial model for fine tuning each mono-lingual track. In the second stage, training of each mono-lingual track is performed with different hyperparameters and we select the final model based on loss of validation data of corresponding track.
\\
As shown in Figure \ref{fig:mainmodel}, first of all model tokenizer tokenize the input sentence further preprocessing done like \cite{singh-etal-2022-silpa}.
 Thereafter language model generate word embeddings for each token. A linear layer applied on each token embeddings which project this embedding into logits, a vector of 67 dimension since total unique annotations in  dataset is 67. Further, for classification, we have used cross-entropy loss function.
%<> and CRF <> which gives log likelihood of the given sequence of tags.












\section{Experimental setup} Our best score achieved with  Muril setup for hindi and bangla track  and for other track best score achieved with XLM-R setup.
Detail about both the setup defined in next subsection. Implementation of our task with Cross entropy classification layer is done by xxxtokenclassificaion class defined in \cite{wolf2020huggingfaces}, where xxx refere to selected model . 
%While implementation with CRF layer used pytorch library.  

\subsection{ Hindi and Bangla track}
Muril model selected for HIndi and Bangla track referring to \cite{singh-etal-2022-silpa}. Muril \cite{https://doi.org/10.48550/arxiv.2103.10730} pretrained on text in 17 languages which include English, Hindi and Bangla with explicitly augmented monolingual text corpora with trans-
lated and transliterated document pairs. Therefor for this setup, in first stage, we combined English, Hindi and Bangla track dataset for training and validation. Based on validation error after fine tuning for 10 epochs, Muril multi-lingual checkpoint was constructed. Further in the second stage Muril multi-lingual checkpoint utilized for fine tuning three mono-linghal tracks separately. Again best model selected by validation loss after 20 epochs of training. We trained model in both stage with 1e-6, 5e-6 and 1e-5 learning rate and 16, 32 and 64 batch size. Hyper parameters for best model showed in Table \ref{tab:hyperparameter}.
\subsection{ Other mono-lingual track} In this setup XLM-R \cite{conneau2020unsupervised} selected as baseline model since this model have strong cross-linguality transferability over more than 100 languages and it cover all mono-lingual tracks. Similar to previous muril setup, this setup also done in two stage. In first stage, all tracks are combinedly traind for 10 epochs and xlm-r multilingual checkpoint constructed. In second stage xlm-r multi-lingual checkpoint utilized for fine tuning of all individual mono-lingual track  for the our task. We trained mode in both stage with 1e-6, 5e-6 and 1e-5 learning rate and 16,32 and 64 batch size. Hyper parameters for best model for this setup shown in Table \ref{tab:hyperparameter} .
\begin{table}[h]
\begin{center}
\begin{tabular}{ |l|c|c|c|c| } 
\hline
\bf Track &  \bf   F1(FG)   & \bf   F1(CG)   & \bf  Rank \\
\hline
Hindi & 74.32&87.02 & 7 \\ 
\hline
Bangla &  73.22&86.91 & 9 \\ 
\hline
Swedish &  65.0&82.54 & 10 \\ 
\hline
German &  64.92&83.08 & 10 \\ 
\hline
Spanish &  62.9&79.26 & 11 \\ 
\hline
Ukrainian &  63.18&80.46 & 11 \\ 
\hline
Italian &  63.11&80.53 & 12 \\ 
\hline
French &  62.39&77.99 & 12 \\ 
\hline
Portugese &  61.05&80.35  & 12 \\ 
\hline
Chinese &  51.65&69.59 & 17 \\ 
\hline
English &  60.85&77.34 & 19 \\ 
\hline
\end{tabular}
\caption {Fine-grained and course-grained macro F1 score(\%)} \label{tab:alltrackresult}
\end{center}
\end{table}
\section{Results and Analysis}
\label{sec:results}
Our best macro F1 score with official rank for 11 tracks tabulated in Table \ref{tab:alltrackresult}. Our system scored 7th rank for Hindi track with 74.32\% F1 score and 9th rank for Bangla track with 73.22\% F1 score. Both the scores achieved by muril setup. For all the other track best score achieved by xlm-r setup.

\begin{table}
\begin{center}
\begin{tabular}{|P{3.5cm}|P{2.2cm}|}
\hline
 \textbf{Entity} & \textbf{Average F1-score}  \\
 \hline
Facility &0.6825 \\ \hline
OtherLOC & 0.5768\\ \hline
HumanSettlement & \textbf{0.8814}\\ \hline
Station &0.7678  \\ \hline
VisualWork & 0.7756 \\ \hline
MusicalWork &0.6692 \\ \hline
WrittenWork & 0.7235\\ \hline 
ArtWork &0.4121\\ \hline
Software & 0.7677\\ \hline
MusicalGRP &0.7700\\ \hline
PublicCorp &0.6533\\ \hline
PrivateCorp & 0.2969\\ \hline
AerospaceManufacturer &0.4472 \\ \hline
SportsGRP &0.8306 \\ \hline
CarManufacturer & 0.6987\\ \hline
ORG &0.6786  \\ \hline
Scientist & 0.4262\\ \hline
Artist &  0.7829\\ \hline
Athlete & 0.7590 \\ \hline
Politician &0.5807  \\ \hline
Cleric & 0.5686 \\ \hline
SportsManager & 0.5357 \\ \hline
OtherPER & 0.4739 \\ \hline
Clothing & 0.5278 \\ \hline
Vehicle &0.6177  \\ \hline
Food & 0.61991 \\ \hline
Drink & 0.6151 \\ \hline
OtherPROD & 0.6036\\ \hline
Medication/Vaccine &0.7503 \\ \hline 
MedicalProcedure &0.6887 \\ \hline
AnatomicalStructure &  0.7186\\ \hline
Symptom &0.4471 \\ \hline
Disease &0.7285  \\ \hline
\end{tabular}
\caption{Average of entity-wise F1-score for 11 monolangual tracks } 
\label{tab:entitywiseaverage}
\end{center}
\end{table}
\begin{table*}[ht]
\centering
%\begin{adjustbox}{width=1.3\textwidth}
%\small
\scriptsize
%\footnotesize

\begin{tabular}{ | c | l | l | l | l | l | l | l | l | l | l | l |}
    \hline
  %   \multicolumn{12} { | c | }{\textbf{F1-Score Results }}\\
  % \hline
 \textbf{Class} & \textbf{BN} & \textbf{ZH} & \textbf{DE} & \textbf{ES} & \textbf{UK} & \textbf{IT} & \textbf{FR} & \textbf{PT} & \textbf{HI} & \textbf{EN} & \textbf{SV} \\
  \hline
Facility & 0.7615 & 0.5762 & 0.6832 & 0.6434 & 0.6621 & 0.7241 & 0.657 & 0.6752 & 0.7358 & 0.6471 & 0.7428 \\ \hline
OtherLOC & 0.6459 & 0.4346 & 0.4666 & 0.4673 & 0.5722 & 0.5021 & 0.4933 & 0.6768 & 0.6654 & 0.5256 & 0.8959 \\ \hline
HumanSettlement & 0.9404 & 0.7704 & 0.8995 & 0.8668 & 0.882 & 0.8637 & 0.8317 & 0.8872 & 0.9411 & 0.8796 & 0.9334 \\ \hline
Station & 0.8924 & 0.7843 & 0.7695 & 0.7308 & 0.7318 & 0.7108 & 0.7402 & 0.7392 & 0.8792 & 0.7649 & 0.703 \\ \hline
VisualWork & 0.8213 & 0.5986 & 0.7636 & 0.7425 & 0.766 & 0.8972 & 0.8365 & 0.7588 & 0.8634 & 0.6745 & 0.8097 \\ \hline
MusicalWork & 0.6221 & 0.4736 & 0.7833 & 0.7045 & 0.6626 & 0.8162 & 0.6692 & 0.7689 & 0.3957 & 0.702 & 0.7632 \\ \hline
WrittenWork & 0.8457 & 0.6636 & 0.7848 & 0.7148 & 0.6949 & 0.678 & 0.7188 & 0.675 & 0.8495 & 0.6536 & 0.6802 \\ \hline 
ArtWork & 0.3448 & 0.3772 & 0.6647 & 0.4564 & 0.3255 & 0.5793 & 0.5001 & 0.132 & 0.3986 & 0.4951 & 0.2601 \\ \hline
Software & 0.8963 & 0.5294 & 0.7788 & 0.8032 & 0.8133 & 0.7401 & 0.7437 & 0.7697 & 0.8773 & 0.7008 & 0.7921 \\ \hline
MusicalGRP & 0.8486 & 0.5863 & 0.7461 & 0.7717 & 0.8393 & 0.7966 & 0.7436 & 0.7744 & 0.883 & 0.6765 & 0.804 \\ \hline
PublicCorp & 0.7825 & 0.4448 & 0.6101 & 0.6774 & 0.6921 & 0.6358 & 0.6294 & 0.7356 & 0.7804 & 0.5862 & 0.6128 \\ \hline
PrivateCorp & 0.7982 & 0.2086 & 0.3086 & 0.1928 & 0.0705 & 0.1919 & 0.3675 & 0.0062 & 0.6462 & 0.2193 & 0.2564 \\ \hline
AerospaceManufacturer & 0.5556 & 0.5455 & 0.7016 & 0.4172 & 0.3909 & 0.3166 & 0.5204 & 0.2647 & 0.5422 & 0.483 & 0.1823 \\ \hline
SportsGRP & 0.9413 & 0.7579 & 0.882 & 0.7765 & 0.8391 & 0.7537 & 0.7609 & 0.7972 & 0.9624 & 0.8137 & 0.8522 \\ \hline
CarManufacturer & 0.8632 & 0.4691 & 0.6578 & 0.7184 & 0.7457 & 0.5974 & 0.678 & 0.706 & 0.8981 & 0.6668 & 0.6852 \\ \hline
ORG & 0.873 & 0.5829 & 0.6892 & 0.6394 & 0.6693 & 0.5917 & 0.5992 & 0.6635 & 0.8659 & 0.614 & 0.6769 \\ \hline
Scientist & 0.435 & 0.3367 & 0.401 & 0.4428 & 0.4491 & 0.4516 & 0.4301 & 0.3912 & 0.474 & 0.4515 & 0.4254 \\ \hline
Artist & 0.7908 & 0.7062 & 0.762 & 0.7888 & 0.7611 & 0.8619 & 0.8085 & 0.7855 & 0.7772 & 0.7931 & 0.7768 \\ \hline
Athlete & 0.7542 & 0.6894 & 0.7362 & 0.7525 & 0.8288 & 0.8323 & 0.7566 & 0.667 & 0.8361 & 0.7747 & 0.7221 \\ \hline
Politician & 0.668 & 0.461 & 0.5694 & 0.561 & 0.4772 & 0.5562 & 0.6182 & 0.5545 & 0.7203 & 0.5596 & 0.6424 \\ \hline
Cleric & 0.6323 & 0.357 & 0.4525 & 0.5946 & 0.5566 & 0.6448 & 0.5737 & 0.6404 & 0.6864 & 0.5241 & 0.5926 \\ \hline
SportsManager & 0.5386 & 0.4206 & 0.46 & 0.5624 & 0.5367 & 0.6454 & 0.5341 & 0.5157 & 0.6855 & 0.5521 & 0.4425 \\ \hline
OtherPER & 0.4216 & 0.4063 & 0.47 & 0.5278 & 0.5604 & 0.4355 & 0.4622 & 0.4963 & 0.4906 & 0.4044 & 0.5386 \\ \hline
Clothing & 0.3529 & 0.4361 & 0.4902 & 0.5662 & 0.5579 & 0.4784 & 0.5458 & 0.4298 & 0.7513 & 0.5628 & 0.6344 \\ \hline
Vehicle & 0.7773 & 0.5376 & 0.6267 & 0.6115 & 0.6277 & 0.5831 & 0.4854 & 0.5758 & 0.8276 & 0.5144 & 0.6279 \\ \hline
Food & 0.7074 & 0.5157 & 0.6528 & 0.5965 & 0.6589 & 0.5714 & 0.5633 & 0.6093 & 0.7249 & 0.5597 & 0.6592 \\ \hline
Drink & 0.7687 & 0.2814 & 0.5 & 0.6633 & 0.6543 & 0.6202 & 0.5877 & 0.6612 & 0.7516 & 0.5787 & 0.6994 \\ \hline
OtherPROD & 0.7128 & 0.4736 & 0.6208 & 0.5573 & 0.6088 & 0.5516 & 0.5659 & 0.6591 & 0.7093 & 0.508 & 0.6731 \\ \hline
Medication/Vaccine & 0.8064 & 0.6206 & 0.7924 & 0.7356 & 0.7923 & 0.7564 & 0.7325 & 0.7403 & 0.8126 & 0.7343 & 0.7299 \\ \hline 
MedicalProcedure & 0.846 & 0.5545 & 0.7603 & 0.6569 & 0.6317 & 0.6898 & 0.6747 & 0.6566 & 0.8006 & 0.6399 & 0.6653 \\ \hline
AnatomicalStructure & 0.8564 & 0.6289 & 0.7716 & 0.7062 & 0.7498 & 0.6405 & 0.625 & 0.6663 & 0.8377 & 0.6782 & 0.7447 \\ \hline
Symptom & 0.7602 & 0.2024 & 0.3818 & 0.4225 & 0.3482 & 0.4375 & 0.4805 & 0.3489 & 0.61 & 0.4602 & 0.4663 \\ \hline
Disease & 0.9003 & 0.6142 & 0.7857 & 0.6881 & 0.6913 & 0.6734 & 0.6549 & 0.718 & 0.8473 & 0.6829 & 0.758 \\ \hline

\end{tabular}
%\end{adjustbox}
\caption {Entity-wise F1-score for 11 monolangual tracks} \label{tab:entitywise}
\end{table*} 


\textbf{ Muril setup vs xlm-r setup for Hindi, Bangla and English} 
Results shown in Table \ref{tab:threetrackresult} indicate that Muril setup better than xlm-r setup for Hindi and Bangla track while for English track xlm-r setup perform better compare to Muril setup.\\

\begin{table}[h]
\begin{center}
\begin{tabular} {| p {1.5 cm} | p {1.1 cm} | p {1.1 cm} | p {1.2 cm} |}
    \hline
    
    \textbf{setup} & \textbf{Hindi} & \textbf{Bangla} & \textbf{English} \\ \hline
    
     xlm-r & 64.25 & 62.37 & \textbf{60.85} \\ \hline    
     Muril & \textbf{74.32} & \textbf{73.22} & 54.88 \\ \hline
     
% \\
\end{tabular}

\caption {F1 score(\%) for xlm-r and Muril setup} \label{tab:threetrackresult}
\end{center}
\end{table}
\\


\textbf{ Clean vs noisy test data} 
 Table\ref{tab:cleanvsnoisy} showed that our system performs lower on noisy data compared to clean data. This might be possible because training data was structured data and we didn't augment other data for robust training.\\

 
 \begin{table}[h]
\begin{center}
\begin{tabular}{ |l|c|c|c|c| } 
\hline
\bf Track &  \bf   Clean F1   & \bf   Noisy F1   & \bf  F1 \\
\hline


Swedish &67.15&60.87&  65.0 \\ 
\hline

Spanish &64.88&58.77&  62.9  \\ 
\hline

Italian & 64.53&60.13& 63.11 \\ 
\hline
French & 64.4&58.04& 62.39 \\ 
\hline
Portugese & 63.07&57.23& 61.05  \\ 
\hline
Chinese & 54.65&42.11& 51.65 \\ 
\hline
English &62.59&56.96&  60.85 \\ 
\hline
\end{tabular}
\caption {Clean and Noisy set macro F1 score(\%) } \label{tab:cleanvsnoisy}
\end{center}
\end{table}

 \textbf{ Fine-grained vs coarse-grained } 
 Table \ref{tab:alltrackresult} showed that macro F1 score for coarse-grained classes is better than fine-grained classes. This infer that the model mispredicted the fine-grained classes while correctly predicting the coarse-grained classes for some inputs.

 \textbf{ Entity-wise evaluation}
 Entity-wise F1 score for all track tabulated in Table \ref{tab:entitywise}. Table \ref{tab:entitywiseaverage} showed average of entity-wise F1-score for 11 mono-lingual tracks. Average F1 score of entity HumanSettlement is best (0.8814) among all other entity, this entity is part of Location entity which showed good result in previous year multiconer task but it is also has observed that OtherLOC entity has 0.5768 F1 score and it is also part of Location Entity. With empirical results we can infer that the model is confused during prediction of OtherLOC with other Location entities. 











\section{Conclusion}


In this paper, we performed multi-lingual track knowledge utilization for mono-lingual track for the MultiCoNER \rom{2} shared task. Our team achieved the top 10 on 4 tracks. We show that the performance of monolingual models can be enhanced by two-stage fine-tuning by allowing them to learn from the training data of all the languages. 
 Also we have compared two cross-lingual language models,  Muril and XLM-R for multi-lingual knowledge transferability for Hindi, Bangla and English track of this task. Muril performed better for Hindi and Bangla tracks although XLM-R performed better for English track. For future work we plan to explore more language models for utilizing their knowledge for NER and other NLP tasks. 








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