SlowGuess/ACL_2020_OCR huggingface dataset:
<sup>6</sup>We expect to see further performance improvement by considering pre-trained language models (Radford et al., 2018; Devlin et al., 2019) for ProductEnc, which we leave for future work.
3For simplicity in notation, we define Eq 12 for a single product. Defining for all training products is straightforward.
Attr.	Model	Vocab	Cov	Micro F1	Micro Prec	Micro Rec	Macro F1	Macro Prec	Macro Rec
Flavor	OpenTag	6,756	73.2	57.5	70.3	49.6	54.6	68.0	47.3
TXtract	13,093	83.9 ↑14.6%	63.3 ↑10.1%	70.9 ↑0.9%	57.8 ↑16.5%	59.3 ↑8.6%	68.4 ↑0.6%	53.8 ↑13.7%
Scent	OpenTag	10,525	75.8	70.6	87.6	60.2	59.3	79.7	50.8
TXtract	13,525	83.2 ↑9.8%	73.7 ↑4.4%	86.1 ↓1.7%	65.7 ↑9.1%	59.9 ↑10.1%	78.3 ↓1.8%	52.1 ↑2.6%
Brand	OpenTag	48,943	73.1	63.4	81.6	51.9	51.7	75.1	41.5
TXtract	64,704	82.9 ↑13.4%	67.5 ↑6.5%	82.7 ↑1.3%	56.5 ↑8.1%	55.3 ↑7.0%	75.2 ↑0.1%	46.8 ↑12.8%
Ingre.d.	OpenTag	9,910	70.0	35.7	46.6	29.1	20.9	34.6	16.7
TXtract	18,980	76.4 ↑9.1%	37.1 ↑3.9%	48.3 ↑3.6%	30.1 ↑3.3%	24.2 ↑15.8%	37.4 ↑8.1%	19.8 ↑18.6%
Average relative increase	↑11.7%	↑6.2%	↑1.0%	↑9.3%	↑10.4%	↑6.8%	↑11.9%
Table 2: Extraction results for flavor, scent, brand, and ingredients across 4,000 categories. Across all attributes, TXtract improves OpenTag by  $11.7\%$  in coverage,  $6.2\%$  in micro-average F1, and  $10.4\%$  in macro-average F1.
introduced additional strong baselines:
1. "OpenTag": the model of Zheng et al. (2018). It is a special case of our system that consists of the ProductEnc and CRF components without leveraging the taxonomy.
2. "Title+": a class of models for conditional attribute value extraction, where the taxonomy is introduced by artificially appending extra tokens  $x_{1}^{\prime}, \ldots, x_{T^{\prime}}^{\prime}$  and a special separator token (<SEP>) to the beginning of a product's text, similar to Johnson et al. (2017):
$$
x ^ {\prime} = \left(x _ {1} ^ {\prime}, \dots , x _ {T ^ {\prime}} ^ {\prime}, <   \mathrm {S E P} >, x _ {1}, \dots , x _ {T}\right)
$$
Tokens  $x_{1}^{\prime},\ldots ,x_{T^{\prime}}^{\prime}$  contain category information such as unique category id ("Title+id"), category name ("Title+name"), or the names of all categories in the path from the root to the category node, separated by an extra token.  $\langle \mathrm{SEP2}\rangle$  ("Title+path").
3. "Concat-*": a class of models for taxonomy-aware attribute value extraction that concatenate the category embedding to the word embedding (-wemb) or hidden BiLSTM embedding layer (-LSTM) instead of using conditional self-attention. We evaluate Euclidean embeddings ("Concat-*-Euclidean") and Poincaré embeddings ("Concat-*-Poincaré").
4. "Gate": a model that leverages category embeddings  $e_{c}$  in a gating layer (Cho et al., 2014; Ma et al., 2019):  $\tilde{h}_{t} = h_{t} \otimes \sigma(W_{4}h_{t} + W_{5}e_{c})$ , where  $W_{4} \in \mathbb{R}^{p \times d}$ ,  $W_{5} \in \mathbb{R}^{p \times m}$  are trainable matrices, and  $\otimes$  denotes element-wise multiplication. Our conditional self-attention is different as it leverages pairwise instead of single-token interactions with category embeddings.
5. "CondSelfAtt": the model with our conditional self-attention mechanism (Section 4.1.3). CondSelfAtt extracts attribute values but does not predict the product category.
6. "MT-*": a multi-task learning model that jointly performs (not taxonomy-aware) attribute value extraction and category prediction. "MT-flat" assumes "flat" categories, whereas "MT-hier" considers the hierarchical structure of the taxonomy (Section 4.2.3).
7. "TXtract": our model that jointly performs taxonomy-aware attribute value extraction (same as CondSelfAtt) and hierarchical category prediction (same as MT-hier).
Here, we do not report previous models (e.g., BiLSTM-CRF) for sequence tagging (Huang et al., 2015; Kozareva et al., 2016; Lample et al., 2016), as OpenTag has been shown to outperform these models in Zheng et al. (2018). Moreover, when considering attributes separately, the model of Xu et al. (2019) is the same as OpenTag, but with a different ProductEnc component; since we use the same ProductEnc for all alternatives, we expect/observe the same trend and do not report its performance.
Table 2 reports the results across all categories. For detailed results see Figure 6 in Appendix. Over all categories, our taxonomy-aware TXtract substantially improves over the state-of-the-art OpenTag by up to  $10.1\%$  in Micro F1,  $14.6\%$  in coverage, and  $93.8\%$  in vocabulary (for flavor).
Table 3 shows results for the four domains of our taxonomy under different training granularities: training on all domains versus training only on the target domain. Regardless of the configuration, TXtract substantially outperforms OpenTag, showing the general advantages of our approach. Interestingly, although training a single model on all of the four domains obtains lower F1 for Flavor, it obtains better results for Scent: training fewer models does not necessarily lead to
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Domain	Attr.	OpenTag/TXtract Micro F1
Train	Test
all Grocery	Grocery Grocery	Flavor	60.3 / 64.9 ↑7.6%
65.4 / 70.5 ↑7.8%
all Baby	Baby Baby	Flavor	54.4 / 63.0 ↑15.8%
69.2 / 71.8 ↑3.8%
all Beauty	Beauty Beauty	Scent	76.9 / 79.5 ↑3.4%
76.9 / 79.0 ↑2.7%
all Health	Health Health	Scent	63.0 / 69.1 ↑9.7%
60.9 / 63.5 ↑4.3%
lower quality and may actually improve extraction by learning from neighboring taxonomy trees.
Table 4 reports the performance of several alternative approaches for flavor value extraction across all categories. OpenTag does not leverage the product taxonomy, so it is outperformed by most approaches that we consider in this work.
Implicit vs. explicit conditioning on categories. "Title+*" baselines fail to leverage the taxonomy, thus leading to lower F1 score than OpenTag: implicitly leveraging categories as artificial tokens appended to the title is not effective in our setting.
Representing the taxonomy with category embeddings leads to significant improvement over OpenTag and "Title+*" baselines: even simpler approaches such as "Concat-*-Euclidean" outperform OpenTag across all metrics. However, "Concat-*" and "Gate-*" do not leverage category embeddings as effectively as "CondSelfAtt": conditioning on the category embedding for the computation of the pair-wise attention weights in the self-attention layer appears to be the most effective approach for leveraging the product taxonomy.
Multi-task Learning. In Table 4, both MT-flat and MT-hier, which do not condition on the product taxonomy, outperform OpenTag on attribute value extraction: by learning to predict the product category, our model implicitly learns to condition on the product category for effective attribute value extraction. MT-hier outperforms MT-flat: leveraging the hierarchical structure of the taxonomy is more effective than assuming flat categories. Table 5 shows that category prediction is more effective when considering the hierarchi
Table 3: Evaluation results for each domain under training configurations of different granularity. TXtract outperforms OpenTag under all configurations.
Model	TX	MT	Micro F1
OpenTag	-	-	57.5
Title+id	✓	-	55.7 ↓3.1%
Title+name	✓	-	56.9 ↓1.0%
Title+path	✓	-	54.3 ↓5.6%
Concat-wemb-Euclidean	✓	-	60.1 ↑4.5%
Concat-wemb-Poincaré	✓	-	60.6 ↑5.4%
Concat-LSTM-Euclidean	✓	-	60.1 ↑4.5%
Concat-LSTM-Poincaré	✓	-	60.8 ↑5.7%
Gate-Poincaré	✓	-	60.6 ↑5.4%
CondSelfAtt-Poincaré	✓	-	61.9 ↑7.7
MT-flat	-	✓	60.9 ↑5.9%
MT-hier	-	✓	61.5 ↑7.0%
Concat & MT-hier	✓	✓	62.3 ↑8.3%
Gate & MT-hier	✓	✓	61.1 ↑6.3%
CondSelfAtt & MT-hier	✓	✓	63.3 ↑10.1%
Table 4: Ablation study for flavor extraction across 4,000 categories. "TX" column indicates whether the taxonomy is leveraged for attribute value extraction (Section 4.1). "MT" column indicates whether multitask learning is used (Section 4.2).
Category Prediction	AUPR	F1	Prec	Rec
Flat	0.61	53.9	74.2	48.0
Hierarchical	0.68	62.7	80.4	56.9
Table 5: Performance of product classification to the 4,000 nodes in the taxonomy using flat versus hierarchical multi-task learning.
cal structure of the categories into our taxonomy-aware loss function than assuming flat categories.
5.4 Visualization of Poincaré Embeddings
Poincaré embeddings effectively capture the hierarchical structure of the product taxonomy: Figure 3a plots the embeddings of product categories in the 2-dimensional Poincaré disk.7 Figure 3b plots the embeddings trained in the 50-dimensional Poincaré ball and projected to the 2-dimensional Euclidean space through tSNE (Maaten and Hinton, 2008).
5.5 Examples of Extracted Attribute Values
Figure 4 shows examples of product titles and attribute values extracted by OpenTag or TXtract. TXtract is able to detect category-specific values: in Figure 4a, "Purple Lemonade" is a valid flavor for "Vitamin Pills" but not for most of other categories. OpenTag, which ignores product categories, fails to detect this value while TXtract
<sup>7</sup>We train 2-dimensional Poincaré embeddings only for visualization. In our experiments we use  $d = 50$  dimensions.
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Title = Controlled Labs Purple Wraith 90 Servings - Purple Lemonade
ASIN=B00CX96KTQ
Category = Vitamins & Dietary Supplements
OpenTag (flavor) = (empty)
TXtract(flavor) = "purple lemonade"
Title = Mason Vitamins Melatonin 500 mcg Fast Meltz Tablets, Fruit, 60 Count
Figure 4: Examples of extracted attribute values from OpenTag and TXtract.
$\mathbf{ASIN} = \mathbf{B015K3Y728}$
TXtract (flavor) = "fruit"
Title = Click - Espresso Protein Drink Vanilla Latte - 16 oz.
ASIN = B005P0LKTU
Category = Sports Nutrition
OpenTag (flavor) = "espresso"
TXtract (flavor) = "vanilla latte"
Title = HP95(TM) Fashion Glitter Matte Eye Shadow Powder Palette Single Shimmer Eyeshadow (10#)
ASIN = B07BBM5B33  
Category = Eyeshadow
OpenTag (scent) = palette
TXtract (scent) = (empty)
successfully extracts it as a flavor. TXtract also learns attribute applicability: in Figure 4d, OpenTag erroneously extracts "palette" as scent for an "Eyeshadow" product, while this product should not have scent; on the other hand, TXtract, which considers category embeddings, does not extract any scent values for this product.
We present a novel method for large-scale attribute value extraction for products from a taxonomy with thousands of product categories. Our proposed model, TXtract, is both efficient and effective: it leverages the taxonomy into a deep neural network to improve extraction quality and can extract attribute values on all categories in parallel.
TXtract significantly outperforms state-of-the-art approaches and strong baselines under a taxonomy with thousands of product categories. Interesting future work includes applying our techniques to different taxonomies (e.g., biomedical) and training a model for different attributes.
The authors would like to sincerely thank Ron Benson, Christos Faloutsos, Andrey Kan, Yan Liang, Yaqing Wang, and Tong Zhao for their insightful comments on the paper, and Gabriel Blanco, Alexandre Manduca, Saurabh Deshpande, Jay Ren, and Johanna Umana for their constructive feedback on data integration for the experiments.
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Mart'in Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pages 265-283.
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, and Pietro Perona. 2019. Task2vec: Task embedding for meta-learning. arXiv preprint arXiv:1902.03545.
Hector Martinez Alonso and Barbara Plank. 2017. When is multitask learning effective? semantic sequence prediction under varying data conditions. In EACL 2017-15th Conference of the European Chapter of the Association for Computational Linguistics, pages 1-10.
Rich Caruana. 1997. Multitask learning. Machine learning, 28(1):41-75.
Jason PC Chiu and Eric Nichols. 2016. Named entity recognition with bidirectional lstm-cnns. Transactions of the Association for Computational Linguistics, 4:357-370.
Kyunghyun Cho, Bart van Merrienboer, Caglar Gul- cehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724- 1734.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1126-1135. JMLR.org.
Rayid Ghani, Katharina Probst, Yan Liu, Marko Krema, and Andrew Fano. 2006. Text mining for product attribute extraction. ACM SIGKDD Explorations Newsletter, 8(1):41-48.
Vishrawas Gopalakrishnan, Suresh Parthasarathy Iyengar, Amit Madaan, Rajeev Rastogi, and Srinivasan Sengamedu. 2012. Matching product titles using web-based enrichment. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 605-614. ACM.
Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991.
Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al. 2017. Google's multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5:339-351.
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Zornitsa Kozareva, Qi Li, Ke Zhai, and Weiwei Guo. 2016. Recognizing salient entities in shopping queries. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 107-111.
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In Proceedings of NAACL-HLT, pages 260-270.
Lin Lan, Zhenguo Li, Xiaohong Guan, and Pinghui Wang. 2019. Meta reinforcement learning with task embedding and shared policy. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pages 2794-2800. International Joint Conferences on Artificial Intelligence Organization.
Xiao Ling and Daniel S Weld. 2012. Fine-grained entity recognition. In Twenty-Sixth AAAI Conference on Artificial Intelligence.
Chen Ma, Peng Kang, and Xue Liu. 2019. Hierarchical gating networks for sequential recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-sne. Journal of machine learning research, 9(Nov):2579-2605.
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pages 1003-1011. Association for Computational Linguistics.
David Nadeau and Satoshi Sekine. 2007. A survey of named entity recognition and classification. *Lingvisticae Investigationes*, 30(1):3-26.
Maximillian Nickel and Douwe Kiela. 2017. Poincaré embeddings for learning hierarchical representations. In Advances in neural information processing systems, pages 6338-6347.
8498
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543.
Petar Petrovski and Christian Bizer. 2017. Extracting attribute-value pairs from product specifications on the web. In Proceedings of the International Conference on Web Intelligence, pages 558-565. ACM.
Duangmanee Pew Putthividhya and Junling Hu. 2011. Bootstrapped named entity recognition for product attribute extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1557-1567. Association for Computational Linguistics.
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. https://blog.openai.com/language-unsupervised.
Martin Rezk, Laura Alonso Alemany, Lasguido Nio, and Ted Zhang. 2019. Accurate product attribute extraction on the field. In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pages 1862-1873. IEEE.
Sebastian Ruder. 2019. Neural Transfer Learning for Natural Language Processing. Ph.D. thesis, National University Of Ireland, Galway.
Amit P. Sheth, Axel Ngonga, Yin Wang, Elizabeth Chang, Dominik Slezak, Bogdan Franczyk, Rainer Alt, Xiaohui Tao, and Rainer Unland, editors. 2017. Proceedings of the International Conference on Web Intelligence, Leipzig, Germany, August 23-26, 2017. ACM.
Damir Vandic, Jan-Willem Van Dam, and Flavius Frasincar. 2012. Faceted product search powered by the semantic web. Decision Support Systems, 53(3):425-437.
Huimin Xu, Wenting Wang, Xinnian Mao, Xinyu Jiang, and Man Lan. 2019. Scaling up open tagging from tens to thousands: Comprehension empowered attribute value extraction from product title. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5214-5223.
Vikas Yadav and Steven Bethard. 2018. A survey on recent advances in named entity recognition from deep learning models. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2145-2158.
Zhilin Yang, Ruslan Salakhutdinov, and William W Cohen. 2017. Transfer learning for sequence tagging with hierarchical recurrent networks. In Proceedings of the International Conference on Learning Representations.
Guineng Zheng, Subhabrata Mukherjee, Xin Luna Dong, and Feifei Li. 2018. Opentag: Open attribute value extraction from product profiles. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1049-1058. ACM.
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For reproducibility, we provide details on TXtract configuration (Section A.1). We also report detailed evaluation results (Section A.2).
A.1 TXtract Configuration
We implemented our model in Tensorflow (Abadi et al., 2016) and Keras. To achieve a fair comparison with OpenTag (Zheng et al., 2018), and to ensure that performance improvements stem from leveraging the product taxonomy, we use exactly the same components and configuration as OpenTag for ProductEnc: We initialize the word embedding layer using 100-dimensional pre-trained Glove embeddings (Pennington et al., 2014). We use masking to support variable-length input. Each of the LSTM layers has a hidden size of 100 dimensions, leading to a BiLSTM layer with  $d = 200$  dimensional embeddings. We set the dropout rate to 0.4. For CategoryEnc, we train  $m = 50$ -dimensional Poincaré embeddings. For CondSelfAtt, we use  $p = 50$  dimensions. For Att, we use  $q = 50$  dimensions. For multi-task training, we obtain satisfactory performance with default hyper-parameters  $\gamma = 0.5$ ,  $w = 1$ , while we leave fine-tuning for future work. For parameter optimization, we use Adam (Kingma and Ba, 2014) with a batch size of 32. We train our model for up to 30 epochs and quit training if the validation loss does not decrease for more than 3 epochs.
A.2 Extra Results
Table 6 reports extraction results (of TXtract trained on all domains) for each domain separately. Table 7 reports category classification results for each domain separately. Table 8 reports several evaluation metrics for our ablation study.
8https://keras.io/
We use the public code in provided by Nickel and Kiela (2017): https://github.com/facebookresearch/poincare-embeddings
Figure 5: Snapshot of https://www.amazon.com/dp/B012AE5EP4. This ethernet cable has been erroneously assigned under "Hair Brushes" category. (The assignment can be seen on the top left part of the screenshot.)
Figure 6: Snapshot of https://www.amazon.com/dp/B07BBM5B33. This eye shadow product has been erroneously assigned under "Travel Cases" category. (The assignment can be seen on the top left part of the screenshot.)
Grocery Products	Baby Products	Beauty Products	Health Products
Attr.	Model	Vocab	Cov	miF1	maF1	Vocab	Cov	miF1	maF1	Vocab	Cov	miF1	maF1	Vocab	Cov	miF1	maF1
Flavor	OpenTag	4364	79.6	60.3	59.0	264	53.1	54.4	45.0	832	45.8	41.1	32.0	1296	58.2	53.9	47.0
TXtract	8607	89.1	64.9	62.8	414	72.8	63.0	56.1	1684	61.3	46.5	35.6	2388	71.5	67.3	57.5
Scent	OpenTag	446	75.5	56.8	48.4	593	69.7	35.7	20.3	7007	78.5	76.9	67.9	2479	68.1	63.0	47.5
TXtract	565	87.4	61.2	51.4	589	72.1	38.1	22.0	9048	85.6	79.5	68.4	3322	79.9	69.1	48.2
Brand	OpenTag	5150	68.8	62.9	52.7	11166	72.2	66.0	54.0	15394	77.2	68.8	54.7	17233	71.2	57.8	45.9
TXtract	6944	78.9	67.4	55.1	14965	81.0	72.9	56.2	19821	85.1	72.7	57.2	22974	82.9	60.5	52.4
Ingred.	OpenTag	3402	82.5	40.5	30.1	490	50.7	27.7	22.4	2767	65.1	33.6	26.8	3251	66.7	34.6	29.9
TXtract	6155	87.3	43.1	36.5	835	59.7	30.5	24.3	5539	70.6	32.9	26.6	6451	74.2	36.5	31.2
Table 6: Extraction results for flavor, scent, brand, and ingredients for each of our 4 domains (sub-trees).
Grocery Products	Baby Products	Beauty Products	Health Products
MT type	AUPR	F1	Prec	Rec	AUPR	F1	Prec	Rec	AUPR	F1	Prec	Rec	AUPR	F1	Prec	Rec
flat	45.9	21.4	63.3	13.7	65.9	23.7	68.4	17.4	63.7	62.4	78.8	56.5	49.8	38.8	60.7	32.7
hierarchical	47.3	29.7	68.4	19.9	68.5	29.4	72.6	22.9	72.1	71.5	83.1	66.4	56.3	47.7	74.6	39.8
Table 7: Product category classification results
Model	TX	MT	Vocab	Cov (%)	Micro-average	Macro-average
F1	Prec	Rec	F1	Prec	Rec
OpenTag	-	-	6,756	73.2	57.5	70.3	49.6	54.6	68.0	47.3
Title+id	✓	-	6,400	69.1	55.7	70.6	46.9	53.3	68.9	45.1
Title+name	✓	-	5,328	70.6	56.9	71.2	48.4	54.2	69.1	46.3
Title+path	✓	-	4,608	64.6	54.3	72.0	44.6	51.9	69.1	43.2
Concat-wemb-Euclidean	✓	-	9,768	76.3	60.1	71.6	52.9	57.4	69.0	50.6
Concat-wemb-Poincaré	✓	-	8,684	74.3	60.6	73.4	52.7	57.7	70.2	50.6
Concat-LSTM-Euclidean	✓	-	9,255	75.9	60.1	71.9	52.8	57.5	69.4	50.6
Concat-LSTM-Poincaré	✓	-	8,893	75.2	60.8	72.9	53.2	57.9	70.3	50.9
Gate-Poincaré	✓	-	9,690	77.1	60.6	71.5	53.5	57.7	69.3	51.0
CondSelfAtt-Poincaré	✓	-	12,558	83.1	61.9	68.8	57.0	58.3	66.5	53.1
MT-flat	-	✓	8,699	72.2	60.9	74.7	52.4	57.8	70.3	50.5
MT-hier	-	✓	9,528	73.4	61.5	74.5	53.2	58.3	70.9	51.1
Concat & MT-hier	✓	✓	9,316	74.6	62.3	75.0	54.3	59.0	70.8	52.1
Gate & MT-hier	✓	✓	10,845	80.0	61.1	70.7	54.8	57.9	67.9	51.8
CondSelfAtt & MT-hier (TXtract)	✓	✓	13,093	83.9	63.3	70.9	57.8	59.3	68.4	53.8
Table 8: Results for flavor extraction across all categories. "TX" column indicates whether the taxonomy is leveraged for attribute value extraction (Section 4.1). "MT" column indicates whether multi-task learning is used (Section 4.2).
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