| { | |
| "paper_id": "2021", | |
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| "date_generated": "2023-01-19T12:12:21.210454Z" | |
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| "title": "KU NLP@LT-EDI-EACL2021: A Multilingual Hope Speech Detection for Equality, Diversity, and Inclusion using Context Aware Embeddings", | |
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| "abstract": "Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques using context-aware string embeddings for word representations and Recurrent Neural Network (RNN) and pooled document embeddings for text representation. We have evaluated and compared the three models for each language with different approaches. Our proposed methodology works fine and achieved higher performance than baselines. The highest weighted average F-scores of 0.93, 0.58, and 0.84 are obtained on the task organisers' final evaluation test set. The proposed models are outperforming the baselines by 3%, 2% and 11% in absolute terms for English, Tamil and Malayalam respectively.", | |
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| "text": "Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques using context-aware string embeddings for word representations and Recurrent Neural Network (RNN) and pooled document embeddings for text representation. We have evaluated and compared the three models for each language with different approaches. Our proposed methodology works fine and achieved higher performance than baselines. The highest weighted average F-scores of 0.93, 0.58, and 0.84 are obtained on the task organisers' final evaluation test set. The proposed models are outperforming the baselines by 3%, 2% and 11% in absolute terms for English, Tamil and Malayalam respectively.", | |
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| "text": "In recent years, social media became an integral part of human life and people started spending more time on these platforms. But people are mindful of social media behaviour and putting less personal information in the public domain Mahesan, 2019, 2020a,b) . Now social media behaviors changed quite dramatically and we are living not just in a pandemic, but also in an \"infodemic\", where fake news is becoming more common (Lima et al., 2020) . Conversations on the internet are often a reflection of the conversations that one makes offline.", | |
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| "text": "Mahesan, 2019, 2020a,b)", | |
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| "section": "Introduction", | |
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| "text": "Several AI techniques are adopted to analyse the online comments in social media, which are intensified on the detection of negative com-ments such as hate speech detection, offensive language identification and abusive language detection (Chakravarthi, 2020a) . Hate speech is widely used in media, internet and public discourse which seen or read in the media expressing disapproval, hatred, or aggression towards minorities, could lead to violence and form negative impact on minorities. Hope speech detection is a new task related to Hate speech detection for finding and highlighting positive comments or supporting content, rather than just filtering hostile content . For this task, YouTube comments/posts that offer support, reassurance, suggestions, inspiration and insight are recognized as hope speech. In bilingual and multilingual communities linguistic code-switching occurs in social groups (Chakravarthi et al., 2018 (Chakravarthi et al., , 2019 Chakravarthi, 2020b) . It is incredibly important in many social groups, When an individual uses a group's dialect or accent, the audience is more receptive to the content (Jose et al., 2020; Priyadharshini et al., 2020) . Recently, many researchers are focused on high resource languages using monolingual corpora but less attention is given to code-switching languages especially under resourced languages like Indian languages Mandl et al., 2020) . In our work, we used Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) not only in English but also code-switched Tamil and Malayalam (Chakravarthi, 2020a).", | |
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| "text": "The authors (Puranik et al., 2021; Ghanghor et al., 2021) introduced a novel task for detecting hostility diffusing content from comments in social media, dubbed hope-speech detection. The authors analysed and studied the importance of automatic identification of user-generated hope speech web content that diffuse tension and violence among people in an international crisis. Finally, the obtained results are very promising and automatic recognition of hope speech may also find applications in many other contexts. But they restricted the definition of hope into diffuse tension and violence not considering the other perspectives of hope. Chakravarthi (2020a) constructed a multilingual Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from YouTube for English and two lowresource languages, Malayalam and Tamil. The authors considered much more perspectives support, reassurance, suggestions, inspiration and insight of the hope and EDI. To facilitate future research on encouraging positivity, the authors make this dataset publicly available and created several baselines to benchmark the proposed dataset.", | |
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| "section": "Related Work", | |
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| "text": "This section describes the dataset used for our experiments and technical description of the proposed methodology.", | |
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| "text": "For our work, we used the Shared Task dataset on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021 (Chakravarthi and Muralidaran, 2021). The dataset contains YouTube comments from English, codeswitched Tamil and Malayalam. This is considered a multilingual resource to allow cross-lingual studies and approaches. The corpus consists of a total of 59,354 comments from YouTube videos, where 28,451 comments are in English, 20,198 comments are in Tamil, and the remaining 10,705 comments are in Malayalam. The dataset was manually annotated with three different labels: Hope Speech, Not-Hope Speech and Other languages, where Other languages refer to comments that were not in the intended language. The Figure 1 shows, distribution of three classes in shared task dataset. For our work, we have used the training, validation and test set of the shared task as depicted in Table 1 .", | |
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| "text": "Hope speech detection is a form of text classification, which classify sentences or documents into specified categories. Most current state of art approaches to text classification rely on a technique called text embedding. The embeddings of words in a sentence is used to make a vector representation of the sentence. The sentence embeddings can be achieved in many ways. It could be done by convolutional neural networks (CNN) (Kim, 2014) , by averaging word vectors (Iyyer et al., 2015) or by using Recurrent Neural Networks(RNNs) (Dai and Le, 2015).", | |
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| "text": "In our proposed method we have used Deep contextual and fixed (non-contextual) embeddings to derive word representations. Then, puts them into an RNN or does a pooling operation on overall word embeddings to obtain a text representation. Finally, a softmax layer accepts the text representations to get the actual class label (Akbik et al., 2019b) . We have implemented three models for each language using contextualized string embeddings (flair) in a FLAIR framework (Akbik et al., 2019a) .", | |
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| "text": "Contextualized String Embedding (Flair) : (Akbik et al., 2018) proposed a novel word embedding from internal states of a trained character language model and termed as contextual string embeddings. The two primary factors powering contextual string embeddings (Flair) are, words are trained as characters and embeddings are contextualised by their surrounding text. Hence, it is treated as character language model(charLM) and it perhaps the biggest benefit of using over a wordbased language model when a word has not been seen in the training data. The Flair embeddings are obtained by training two LSTM -based language models, forward and backward. The final word embedding is the concatenation of two specific hidden states from two language models. Let, t 1 , t 2 ...., t n be the character indices. Furthermore, let h f t , h b t be the hidden states at character position t for forward and backward LM respectively. The Flair embedding w k for the word w k is defined as given in Equation 1.", | |
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| "text": "EQUATION", | |
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| "raw_str": "w k = h f t k+1 \u22121 ; h b t k \u22121", | |
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| "text": "Stacked Embedding : In this method we combine different embeddings, such as word2vec, Glove, FastText along with embeddings generated from Flair language models (Akbik et al., 2019a) . ", | |
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| "text": "We have utilized the FLAIR framework (Akbik et al., 2019a) for all our experiments with GPU (12 GB) provided by Google Colab. We have trained three models for each language. The first model employed a pretrained flair LM for word representation and RNN for text representation (FLAIR+RNN). The second model combines pretrained flair LM and pretrained word embeddings (PWE) for word representaion and RNN is used for text representaion (FLAIR+PWE+RNN). The third model apply the same word representation and pooled document embedding for text representation (FLAIR+PWE+Pooled). The Gated Recurrent Unit (GRU) is used for document RNN embedding and max pool operation is used for pooled document embedding. The Table 2 shows the hyperparameters settings for all our experiments. For all three languages, We have adopted pretrianed flair embeddings from FLAIR framework (enforward, en-backward, ml-forward, ml-backward, ta-forward and ta-backward) (Akbik et al., 2019a (Kakwani et al., 2020) .", | |
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| "section": "Experimental Setup", | |
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| "text": "The performance of a text classification model is usually noted as F1-score (harmonic mean of precision and recall) since accuracy can often be misleading in an imbalanced class distribution (Akosa, 2017) . For this task, the dataset having imbalanced class distribution as in Figure 1 . Due to the imbalance problem, we measured our system performance in terms of weighted averaged Precision, weighted averaged Recall and weighted averaged F-Score across all the three classes. Weighted averaged calculations use average of the supportweighted mean per label. ", | |
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| "text": "This section presents the results and analysis of our experiments, which we have explained in the previous sections. As well as, we present the baseline results obtained by the authors (Chakravarthi, 2020a), to compare the performance with our proposed models. The final evaluation results obtained for participants' submissions by task organisers are also presented in overview paper (Chakravarthi and Muralidaran, 2021). The best-performing classifiers on HopeEDI datasets are considered as baseline models for this task (See Table 3 ). The Table 4 depict the precision, recall and F-score results of trained models for three languages described in section 4.1.", | |
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| "text": "As shown, all our proposed models for Hope EDI detection task in three languages work well and show significant improvement than the bestperformed baseline models. The results indicate that a Hope EDI detection classifier with good precision and recall can be constructed using deep learning approaches. It may be due to the representational power of pre-trained word embeddings or language models to capture semantic and lexical structure. It has virtually replaced the feature engineering part of supervised machine learning classifiers.", | |
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| "text": "For English and Malayalam, our model with stacked word embeddings and pooled document embedding achieved the highest performance on the HopeEDI dataset with a weighted average F-Score of 0.93 and 0.84 respectively. But in Tamil, stacked PWE with RNN document embedding performed better than other models with weighted average F-Score of 0.58. Furthermore, it can be seen that all Malayalam language models (0.79,0.83,0.84) achieved greater improvement than baseline models(0.73). Other two languages exhibit slight improvement in proposed models than baseline models.", | |
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| "text": "In most cases, it can be observed from the Table 4 that the flair embedding with PWE gives higher performance than the standalone embeddings. This indicates that combining contextual embedding with non-contextual embeddings achieves noticeably better outcomes. This finding is in line with the findings of (Akbik et al., 2018) . The Figures 2 and 3 represents the class level precision, recall and f-scores of best-performed baseline models and best performed proposed models for all three languages. It can be noticed from these figures that the class \"not-english\" have no significant performance in both models due to the imbalance in shared task dataset (Chakravarthi, 2020a).", | |
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| "text": "Also, we can observe that the \"Non-hopespeech\" class for English shows similar perfor- mance in both models and \"Hope-speech\" class hold a remarkable improvement over baseline models. In Tamil and Malayalam the majority class \"Non-hope-speech\" possesses approximately similar F-scores for baseline and proposed the best model. Another notable and interesting observation from Figure 2 and 3 is that the two minority classes in Malayalam dataset, \"Hope-speech\" and \"not-malayalam\" shows comparable performance approximately 30% above F-score than the baseline model. Also, \"not-Tamil\" class shows approximately 25% above F-score than the baseline model. These higher scores indicate that deep learning techniques can classify the correct class regardless of label distribution better than traditional machine learning technique. This may because our deep learning techniques rely on pre-trained embeddings and language model, which is a contextualized word representation allowing a word to be associated with multiple word vectors, whereas the classical techniques rely on merely manually selected features.", | |
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| "section": "Results and Analysis", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We have presented a deep learning technique with contextual aware embeddings for Hope speech de-tection task in three languages: English, Malayalam and Tamil (code-switched). We have used contextual string embedding (flair) and pre-trained word embeddings (PWE) for word representations and Recurrent Neural Network (RNN) and pooled document embedding with max pool operation for text representations. All the three models were evaluated for each language and compared the baseline models using the dataset given by Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) Shared Task organizers. All model performances are measured using weighted average F-score due to the imbalanced class distribution in the dataset.", | |
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| "section": "Conclusion", | |
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| "text": "We have obtained the highest F-scores 0.93, 0.58 and 0.84 for three languages English, Tamil and Malayalam respectively, which significantly improved performance over baselines (0.90, 0.56, and 0.73). For the minority class 'not-language', the proposed best model improved 35% and 25% performance than baselines for Malayalam and Tamil, respectively. Based on these observations, we conclude that the deep learning models with contextual string embeddings are well suited for HopeEDI detection task with an imbalanced dataset. We could also achieve good performance results with moder-ate resources (one GPU and a small corpus), even without optimizing hyperparameters.", | |
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| "text": "The performance can be improved further by fine-tuning hyperparameters and pre-trained contextual embeddings, incorporating different attention mechanisms and increasing the size of the training dataset.", | |
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| "section": "Conclusion", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "(a) Malayalam (b) Tamil", | |
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| "title": "FLAIR: An easy-to-use framework for state-of-theart NLP", | |
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| "pages": "54--59", | |
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| "raw_text": "Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019a. FLAIR: An easy-to-use framework for state-of-the- art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 54-59, Minneapolis, Minnesota. Association for Computational Linguistics.", | |
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| "FIGREF0": { | |
| "text": "Distribution of three classes over the Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021 Shared Task dataset for English, Malayalam and Tamil languages Document Pool Embeddings : It is a simple document embedding, calculate a pooling operation (mean or max or min) over a list of token embeddings in a document. The default operation is mean which gives the mean of all words in the sentence(Akbik et al., 2019a).", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "text": "Class level precision, recall and f-scores of baselines models (Chakravarthi, 2020a) for three languages. Class level precision, recall and f-scores of best-performed models from our experiments for three languages.", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "TABREF1": { | |
| "type_str": "table", | |
| "text": "Train-Devolopment-Test Split", | |
| "html": null, | |
| "content": "<table/>", | |
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| }, | |
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| "html": null, | |
| "content": "<table><tr><td>: Hyper Parameters</td></tr><tr><td>alam and Tamil langauge used the PWE from In-</td></tr><tr><td>dicFT, FastText-based word embeddings (11 lan-</td></tr><tr><td>guages), which is a 300-dimensional word embed-</td></tr><tr><td>dings for each language on IndicCorp, recently pub-</td></tr><tr><td>lished large monolingual sentence level corpora for</td></tr><tr><td>11 Indian languages</td></tr></table>", | |
| "num": null | |
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| "TABREF5": { | |
| "type_str": "table", | |
| "text": "Baseline results from (Chakravarthi, 2020a).", | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"2\">Language Model</td><td/><td>Weighted avg</td></tr><tr><td/><td/><td>P</td><td>R</td><td>F</td></tr><tr><td/><td>FLAIR+RNN</td><td colspan=\"2\">0.91 0.92 0.91</td></tr><tr><td>English</td><td>FLAIR+PWE+RNN</td><td colspan=\"2\">0.92 0.93 0.91</td></tr><tr><td/><td colspan=\"3\">FLAIR+PWE+Pooled 0.92 0.93 0.93</td></tr><tr><td/><td>FLAIR+RNN</td><td colspan=\"2\">0.58 0.58 0.56</td></tr><tr><td>Tamil</td><td>FLAIR+PWE+RNN</td><td colspan=\"2\">0.60 0.59 0.58</td></tr><tr><td/><td colspan=\"3\">FLAIR+PWE+Pooled 0.62 0.60 0.56</td></tr><tr><td/><td>FLAIR+RNN</td><td colspan=\"2\">0.78 0.82 0.79</td></tr><tr><td colspan=\"2\">Malayalam FLAIR+PWE+RNN</td><td colspan=\"2\">0.82 0.86 0.83</td></tr><tr><td/><td colspan=\"3\">FLAIR+PWE+Pooled 0.84 0.85 0.84</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF6": { | |
| "type_str": "table", | |
| "text": "Our models results from proposed methodology.", | |
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| } | |
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