Proceedings of NAACL HLT 2021: IndustryTrack Papers, pages 296–303June 6–11, 2021. ©2021 Association for Computational Linguistics

296

Label-Guided Learning for Item Categorization in E-commerce

Lei ChenRakuten Institute of Technology

Boston, MAlei.a.chen@rakuten.com

Hirokazu MiyakeRakuten Institute of Technology

Boston, MAhirokazu.miyake@rakuten.com

Abstract

Item categorization is an important applicationof text classification in e-commerce due to itsimpact on the online shopping experience ofusers. One class of text classification tech-niques that has gained attention recently is us-ing the semantic information of the labels toguide the classification task. We have con-ducted a systematic investigation of the poten-tial benefits of these methods on a real dataset from Rakuten, a major e-commerce com-pany in Japan. We found that using pre-trainedword embeddings specialized to specific cate-gories of items performed better than one ob-tained from all available categories despite thereduction in data set size. Furthermore, using ahyperbolic space to embed product labels thatare organized in a hierarchical structure led tobetter performance compared to using a con-ventional Euclidean space embedding. Thesefindings demonstrate how label-guided learn-ing can improve item categorization systemsin the e-commerce domain.

1 Introduction

Natural language processing (NLP) techniqueshave been applied extensively to solve modern e-commerce challenges (Malmasi et al., 2020; Zhaoet al., 2020). One major NLP challenge in e-commerce is item categorization (IC) which refersto classifying a product based on textual informa-tion, typically the product title, into one of numer-ous categories in the product category taxonomytree of online stores. Although significant progresshas been made in the area of text classification,many standard open-source data sets have limitednumbers of classes which are not representative ofdata in industry where there can be hundreds oreven thousands of classes (Li and Roth, 2002; Pangand Lee, 2004; Socher et al., 2013)To cope withthe large number of products and the complexity ofthe category taxonomy, an automated IC system isneeded and its prediction quality needs to be high

Category Taxonomy

ApplianceGift Men's fashionBeverageOther... ...

CoffeeTea Soft drink

Chinesetea

Japanesetea Fruit tea

Root level genre

Leaf level genrecoffeebeans

Instantcoffee

伊藤園 お薄抹茶 華の宴（はなのえん）

Figure 1: Subset of the product taxonomy tree for itemcategorization.

enough to provide positive shopping experiencesfor customers and consequently drive sales. Fig-ure 1 shows an example diagram of the productcategory taxonomy tree for the IC task. In this ex-ample, a tin of Japanese tea 1 needs to be classifiedinto the leaf level category label “Japanese tea.”

As reviewed in Section 2, significant progresshas been made on IC as a deep learning text classifi-cation task. However, much of the progress in textclassification does not make use of the semanticinformation contained in the labels. Recently therehave been increasing interest in taking advantageof the semantic information in the labels to im-prove text classification performance (Wang et al.,2018; Liu et al., 2020; Du et al., 2019; Xiao et al.,2019; Chai et al., 2020). For the IC task, labels in aproduct taxonomy tree are actively maintained byhuman experts and these labels bring rich semanticinformation. For example, descriptive genre infor-mation like “clothes” and “electronics” are usedrather than just using a numeric index for the classlabels. It is reasonable to surmise that leveragingthe semantics of these category labels will improvethe IC models.

Although label-guided learning has been shown

1Image from https://item.rakuten.co.jp/kusurinokiyoshi/10016272/

297

to improve classification performance on severalstandard text classification data sets, its applicationto IC on real industry data has been missing thusfar. Compared to standard data sets, e-commercedata typically contain more complicated label tax-onomy tree structures, and product titles tend to beshort and do not use standard grammar. Therefore,whether label-guided learning can help IC in indus-try or not is an open question worth investigating.

In this paper, we describe our investigation ofapplying label-guided learning to the IC task. Us-ing real data from Rakuten2, we tested two models:Label Embedding Attentive Model (LEAM) (Wanget al., 2018) and Label-Specific Attention Network(LSAN) (Xiao et al., 2019). In addition, to copewith the challenge that labels in an IC task tend tobe similar to each other within one product genre,we utilized label embedding methods that can bet-ter distinguish labels which led to performancegains. This included testing the use of hyperbolicembeddings which can take into account the hier-archical nature of the taxonomy tree (Nickel andKiela, 2017).

The paper is organized as follows: Section 2reviews related research on IC using deep learning-based NLP and the emerging techniques of label-guided learning. Section 3 introduces the twolabel-guided learning models we examined, namelyLEAM and LSAN, as well as hyperbolic embed-ding. Section 4 describes experimental results on alarge-scale data set from a major e-commerce com-pany in Japan. Section 5 summarizes our findingsand discusses future research directions.

2 Related works

Deep learning-based methods have been widelyused for the IC task. This includes the use of deepneural network models for item categorization ina hierarchical classifier structure which showedimproved performance over conventional machinelearning models (Cevahir and Murakami, 2016),as well as the use of an attention mechanism toidentify words that are semantically highly corre-lated with the predicted categories and thereforecan provide improved feature representations for ahigher classification performance (Xia et al., 2017).

Recently, using semantic information carried bylabel names has received increasing attention intext classification research, and LEAM (Wang et al.,2018) is one of the earliest efforts in this direction

2https://www.rakuten.co.jp

that we are aware of. It uses a joint embedding ofboth words and class labels to obtain label-specificattention weights to modify the input features. Ona set of benchmark text classification data sets,LEAM showed superior performance over modelsthat did not use label semantics. An extension ofLEAM called LguidedLearn (Liu et al., 2020) madefurther modifications by (a) encoding word inputsfirst and then using the encoded outputs to com-pute label attention weights, and (b) using a multi-head attention mechanism (Vaswani et al., 2017)to make the attention-weighting mechanism havemore representational power. In a related model,LSAN (Xiao et al., 2019) added a label-specific at-tention branch in addition to a self-attention branchand showed superior performance over models thatdid not use label semantics on a set of multi-labeltext classification tasks.

Alternatively, label names by themselves maynot provide sufficient semantic information for ac-curate text classification. To address this potentialshortcoming, longer text can be generated basedon class labels to augment the original text input.Text generation methods such as using templatesand reinforcement learning were compared, andtheir effectiveness were evaluated using the BERTmodel (Devlin et al., 2019) with both text sen-tence and label description as the input (Chai et al.,2020).

Finally, word embeddings such as word2vec(Mikolov et al., 2013) and GloVe (Penningtonet al., 2014) are generated in Euclidean space. How-ever, embeddings in non-Euclidean space called hy-perbolic embeddings have been developed (Nickeland Kiela, 2017; Chen et al., 2020a,b) and havebeen shown to better represent the hierarchical re-lationship among labels.

3 Model

For a product title X consisting of L words X =[w1, . . . , wL], our goal is to predict one out of a setof K labels, y ∈ C = {c1, . . . , cK}. In a neuralnetwork-based model, the mapping X → y gener-ally consists of the following steps: (a) encodingstep (f0), converting X into a numeric tensor rep-resenting the input, (b) representation step (f1),processing the input tensor to be a fixed-dimensionfeature vector z, and (c) classification step (f2),mapping z to y using a feed-forward layer.

Among label-guided learning models, we choseboth LEAM (Wang et al., 2018) and LSAN (Xiao

298

Step LEAM LSANf0 Word embedding Word embedding + Bi-LSTM encodingf1 Only label-specific attention Both self- and label-specific attentions + adaptive interpolationf2 Softmax with CE loss Softmax with CE loss

Table 1: Comparison of LEAM (Wang et al., 2018) and LSAN (Xiao et al., 2019) with respect to the three modelingsteps.

Cosine scorebetween V,C

1D C

onv

max-pool softmax

labe

l-gui

ded

atte

ntio

n

feature z

V_1

V_2

V_L

title

{w_1

,... w

_L}

C-1

C_K

word embedding

label embeddinglabel-specific attention

K labels

Figure 2: Architecture of LEAM (Wang et al., 2018).

et al., 2019) for our experiments. Table 1 shows acomparison between these models.

3.1 LEAMThe LEAM architecture is shown in Figure 2 (Wanget al., 2018). First a product title of length L is en-coded as V = [v1, . . . , vL] where vl ∈ RD is deter-mined through word embedding and V ∈ RD×L.The class labels are also encoded via label em-bedding as C = [c1, ..., cK ] where K is the totalnumber of labels, ck ∈ RD and C ∈ RD×K . Thelabel embeddings are title-independent and is thesame across all titles for a given set of labels. Wecan then compute the compatibility of each word-label pair based on their cosine similarity to obtaina compatibility tensor G ∈ RL×K .

The compatibility tensor is transformed into anattention vector through the following steps, (a)apply a 1D convolution to refine the compatibilityscores by considering its context, (b) apply maxpooling to keep the maximum score, and (c) ap-ply a softmax operation to obtain the label-guidedattention weights β. These attention weights con-taining the label semantic information are used inthe f1 step to compute a new representation,

z =∑l

βlvl. (1)

After obtaining z, we use a fully-connected layer

with softmax to predict y ∈ C. The entire pro-cess f2(f1(f0(X ))) is optimized by minimizingthe cross-entropy loss between y and f2(z).

3.2 LSANThe LSAN architecture is shown in Figure 3 (Xiaoet al., 2019). As shown in Table 1, LSAN hasa few modifications compared to LEAM. First, abi-directional long short-term memory (Bi-LSTM)encoder is used to better capture context seman-tic cues in the representation. The resulting con-catenated tensor is H = [

−→H,←−H ] where

−→H and

←−H

represent LSTM encoding outputs from forwardand backward directions and H ∈ RL×2P whereP is the dimension of the LSTM hidden state. Formodel consistency we typically set P = D.

Additional features of LSAN which extendLEAM include (a) using self-attention on the en-coding H , (b) creating a label-attention componentfrom H and C, and (c) adaptively merging the self-and label-attention components.

More specifically, the self-attention score A(s) isdetermined as

A(s) = softmax(W2 tanh(W1HT )), (2)

where W1 ∈ Rda×2P and W2 ∈ RK×da are self-attention tensors to be trained, da is a hyperparam-eter, A(s) ∈ RK×L and each row A

(s)j· is an L-

dimensional vector representing the contributions

299

Dot productbetween H,C

labe

l-gui

ded

atte

ntio

n

feature z

V_1

V_2

V_L

title

{w_1

,... w

_L}

C-1

C_K

word embedding

label embedding

K labels

H_1

H_2

H_L

Bi-LSTM

self

atte

ntio

n

adap

tive

com

bina

tion

Figure 3: Architecture of LSAN (Xiao et al., 2019).

of all L words to label j. Therefore,

M (s) = A(s)H (3)

is a representation of the input text weighted byself-attention where M (s) ∈ RK×2P .

From the title encoding H and the label embed-ding C, compatibility scores between class labelsand title words can be computed as the product

←−−A(l) = CT←−HT (4)−−→A(l) = CT−→HT , (5)

where A(l) ∈ RK×L and each row A(l)j· is a L-

dimensional vector representing the contributionsof all L words to label j. The product title can berepresented using label attention as

M (l) = [←−−A(l)←−H,

−−→A(l)−→H ] (6)

where M (l) ∈ RK×2P .The last procedure in the f1 step of LSAN is

to adaptively combine the self- and label-attentionrepresentations M (s) and M (l) as

Mj. = αjM(s)j. + βjM

(l)j. , (7)

where the two interpolation weight factors (α, β ∈RK) are computed as

α = σ(M (s)W3) (8)

β = σ(M (l)W4), (9)

with the constraint αj + βj = 1, W3,W4 ∈ R2P

are trainable parameters, σ(x) ≡ 1/(1+e−x) is theelement-wise sigmoid function, and M ∈ RK×2P .

Although the original LSAN model proposedmultiple additional layers in its f2 step, in our im-plementation we performed average pooling alongthe label dimension and then to a fully-connectedlayer with softmax output, similar to LEAM. Fi-nally, the cross entropy loss is minimized.

3.3 Hyperbolic Embedding

In e-commerce item categorization we tend to use amore complicated label structure with a large num-ber of labels organized as a taxonomy tree com-pared to standard text classification data sets. Oneimmediate issue is that hundreds of labels can existat the leaf level, some with very similar labels like“Japanese tea” and “Chinese tea,” and the differencein label embedding vectors in Euclidean space canbe too small to be distinguished by machine learn-ing models. Such issues become more severe withincreasing taxonomy tree depth as well. Hyper-bolic embedding is one technique that has beendeveloped which can address these issues (Nickeland Kiela, 2017; Chen et al., 2020a,b).

Hyperbolic space is different from Euclideanspace by having a negative curvature. Conse-quently, given a circle, its circumference and discarea grow exponentially with radius. In contrast,in Euclidean space the circumference and areagrow only linearly and quadratically, respectively.For representing hierarchical structures like trees,this property can ensure that all leaf nodes whichare closer to the edge of the circle maintain largeenough distances from each other.

As a specific application, Poincaré embeddinguses the Poincaré ball model which consists ofpoints within the unit ball Bd where the distance

300

Figure 4: (a) Tree with a branching factor of 2 in Eu-clidean space. (b) Embedding a hierarchical tree witha branching factor of 2 in a Poincaré disk. Figure fromFigure 1(b) in (Nickel and Kiela, 2017).

between two points, u,v ∈ Bd is defined as

d(u,v) = cosh−1

(1+2

‖u− v‖2

(1− ‖u‖2)(1− ‖v‖2)

).

(10)The Poincaré embedding is obtained by minimiz-ing a loss function depending only on d(u,v) forall pairs of labels (u,v) using Riemannian opti-mization methods.

Figure 4 illustrates the differences between usingan Euclidean space and a Poincaré ball model whenrepresenting nodes organized in a tree. Using ahyperbolic embedding has the potential to maintainlarge enough distances when our models aim todistinguish subtle differences among these labels.

4 Experiments and Results

4.1 Experimental SetupData set: Our data set consisted of more than onemillion products in aggregate from Rakuten, a largee-commerce platform in Japan, focusing on fourmajor product categories which we call root-levelgenres. Our task, a multi-class classification prob-lem, was to predict the leaf-level product categoriesfrom their Japanese titles. Further details of ourdata set are shown in Table 2.

Evaluation metric: We used the macro-averagedF-score F to evaluate model performance. This isdefined in terms of the per-class F-score Fk as

F =1

K

K∑k=1

Fk, (11)

Fk =2PkRk

Pk +Rk, (12)

where K is the total number of classes, and Pk andRk are the precision and recall for class k.

Pre-trained embedding methods: We tested thefollowing three methods:

• All genre: Word embedding pre-trained on allof the data across different root-level genres;for the label embedding, the average of theword embedding from all word tokens in alabel is used to initialize the label embeddingC and this is further updated in the modeltraining process.

• Genre specific: Word embedding pre-trainedfrom data specific to each root-level genre;label embeddings were obtained similarly tothe all-genre method.

• Poincaré: Label embedding pre-trained onthe Poincaré ball taking into account the fullhierarchical taxonomy tree.

Models: We compared a number of variants ofLEAM and LSAN as described below.

• LEAM: Described in Section 3 (Wang et al.,2018), using all-genre pre-trained word em-beddings.

• LEAMbase: LEAM without the label embed-ding attention component (effectively fixingβl = 1/L in Eq. 1), using all-genre pre-trained word embeddings.

• LSAN: Described in Section 3 (Xiao et al.,2019), using all-genre pre-trained word em-beddings.

• LSANbase: LSAN without the label-specificattention component (effectively fixing β =0 in Eq. 7) which is similar to Atten-tionXML (You et al., 2019), and using all-genre pre-trained word embeddings.

• LSANgenre: LSAN using genre-specific pre-trained word embeddings.

• LSANPoincaré: LSAN using genre-specificpre-trained word embeddings for the titles andpre-trained Poincaré embeddings for the la-bels.

Experimental parameters: Our models were im-plemented in TensorFlow 2.3 using a GPU for train-ing and evaluation. Since Japanese text does nothave spaces to indicate individual words, tokeniza-tion was performed with MeCab, an open source

301

Root genre Class size Train size Dev size Test size Mean words/titleCatalog Gifts & Tickets 29 11,369 1,281 559 31Beverages 32 205,107 22,805 10,315 21Appliances 286 399,584 44,529 18,478 20Men’s Fashion 71 593,126 65,939 43,243 23

Table 2: Summary of our data set obtained from a large e-commerce platform in Japan.

Root genre LEAMbase LEAM LSANbase LSANCatalog Gifts & Tickets 0.341 0.289↓ 0.241 0.338Beverages 0.719 0.755 0.759 0.773Appliances 0.682 0.654↓ 0.667 0.686Men’s Fashion 0.696 0.657↓ 0.685 0.686

Table 3: Macro F-score of LEAM and LSAN without and with label attention.

Japanese part-of-speech and morphological ana-lyzer using conditional random fields (CRF).3 Oncethe text was tokenized, we fixed our input lengthto L = 60 words by truncating the title if it waslonger than L and zero-padding the title if it wasshorter than L. If a word appeared less than threetimes, it was discarded and replaced with an out-of-vocabulary token.

Pre-trained word embeddings of dimensionD =100 using just product titles were obtained withfastText, which uses a skipgram model with bag-of-character n-grams (Bojanowski et al., 2016). Noexternal pre-trained embeddings were used. Afterinitialization of word and label embeddings withpre-trained values, they were jointly trained withthe remaining parameters of the model.

For Poincaré embedding of labels, we used anembedding dimension of 300. Pre-trained Poincaréembeddings of labels were obtained by represent-ing the genre taxonomy tree as (child, parent)pairs and minimizing a loss function which de-pends only on inter-genre distances as defined inEq. 10 (Nickel and Kiela, 2017). These pre-trainedPoincaré label embeddings were used to initializethe label embeddings in LSAN but during trainingwere allowed to vary according to the standard lossoptimization process in Euclidean space.

For LEAM, we used a 1D convolution windowsize of 5. For LSAN, we set da = 50, and whenwe experimented with the Poincaré embedding weset the LSTM hidden state dimension P = 300 tomatch the Poincaré embedding dimension.

The models were trained by minimizing thecross-entropy loss function using the Adam opti-

3https://taku910.github.io/mecab/

mizer with an initial learning rate of 0.001 (Kingmaand Ba, 2015). We used early stopping with a pa-tience of 10 to obtain the final models.

4.2 Results and Discussions

Impact of label attention: We examined the im-pact of label attention by comparing performancewithout and with label attention for LEAM andLSAN for each of the four root-level genres us-ing all-genre pre-trained word embeddings. Theresult is shown in Table 3. For LEAM, we do notobserve consistent improvements by including thelabel attention component, contrary to what waspreviously reported on standard text classificationdata sets (Wang et al., 2018). On the other handfor LSAN we do observe consistent improvementsover all root-level genres by including the labelattention component of the model. Since we didnot observe a consistent improvement for LEAMin using label attention, for the remainder of thissection we focus on variations of LSAN.

Impact of different pre-trained embeddings: Wenext evaluated the impact of using different pre-trained embeddings for the title embeddings aswell as the label embeddings for each of the fourroot-level genres. This is shown in Table 4. Weobserved that different pre-trained embeddings canconsistently have a significant effect on model per-formance. In particular, using genre-specific em-beddings outperformed all-genre embeddings forall genres. This is particularly notable for the small-est genre where we used more than 10 times thedata to obtain the all-genre embeddings.

We believe this is because words that occur inthe same root-level genre will tend to be embeddedcloser to each other in the full embedding space,

302

Root genre LSAN LSANgenre LSANPoincaré

Catalog Gifts & Tickets 0.338 0.403 0.438Beverages 0.773 0.784 0.789Appliances 0.686 0.697 0.701Men’s Fashion 0.686 0.701 0.722

Table 4: Macro F-score of LSAN with various pre-trained title and label embeddings.

which then makes it more difficult for the label at-tention to distinguish between different but similarlabels such as “Japanese tea” and “Chinese tea.”By using pre-trained embeddings obtained fromspecific genres, the embeddings become spaced far-ther apart and therefore the label attention is ableto better distinguish labels with similar names.

Poincaré embeddings take this further by requir-ing the embedding space distance between all leaf-genre labels to be far apart from each other, andour results show that this leads to the best modelperformance. This supports our hypothesis thatthe distance between labels in the label embeddingspace is an important factor in ensuring that labelattention improves model performance.

Compared to models using only the product ti-tles, we see that models using label-guided learningcan significantly improve the F-score. In particular,LSAN using a Poincaré label embedding shows thefollowing F-score increases compared to LSANbase: 19.7% for “Catalog Gifts & Tickets,” 3.0%for “Beverages,” 3.4% for “Appliances,” and 3.7%for “Men’s Fashion.” Note that the largest increasewas achieved on the genre with the fewest traininginstances.

5 Conclusions

Since 2018, there have been increasing interest inthe field of NLP to use the semantic informationof class labels to further improve text classificationperformance. On the item categorization task in e-commerce, a taxonomy organized in a hierarchicalstructure already contains rich meaning and pro-vides an ideal opportunity to evaluate the impact oflabel-guided learning. In this paper, we used realindustry data from Rakuten, a leading Japanesee-commerce platform, to evaluate the benefits oflabel-guided learning.

Our experiments showed that LSAN is superiorto LEAM because of its usage of context encodingand adaptive combination of both self- and label-attention. We also found that using genre-specificpre-trained embeddings led to better model per-

formance than pre-trained embeddings obtainedfrom all product genres. This is likely because pre-training on specific genres allows the embeddingto focus on differences between similar genres andthe label embeddings are able to take advantageof this. Finally, we showed that using hyperbolicembedding, more specifically Poincaré embedding,can improve model performance further by ensur-ing that all class labels are sufficiently separated toallow label-guided learning to work well.

One possible limitation of our current work isthat although the label embedding is initialized us-ing a hyperbolic embedding, the rest of the trainingprocess proceeds in Euclidean space. Future workcould explore the possibility of training the entiremodel in hyperbolic space. Another direction isto incorporate the label-attention mechanism intothe BERT model (Devlin et al., 2019), which hasproven to be a powerful approach to text encoding.In addition, more advanced approaches to obtain-ing better representations of labels on top of ourexisting approach of using word tokens in labelscould be explored.

Acknowledgements

The authors would like to thank Yandi Xia for in-troducing hyperbolic embeddings to us and pre-training the Poincaré embeddings.

ReferencesPiotr Bojanowski, Edouard Grave, Armand Joulin,

and Tomas Mikolov. 2016. Enriching word vec-tors with subword information. arXiv preprintarXiv:1607.04606.

Ali Cevahir and Koji Murakami. 2016. Large-scalemulti-class and hierarchical product categorizationfor an E-commerce giant. In Proceedings of COL-ING 2016, the 26th International Conference onComputational Linguistics: Technical Papers, pages525–535, Osaka, Japan. The COLING 2016 Orga-nizing Committee.

Duo Chai, Wei Wu, Qinghong Han, Fei Wu, and JiweiLi. 2020. Description based text classification withreinforcement learning. In Proceedings of the 37th

303

International Conference on Machine Learning, vol-ume 119 of Proceedings of Machine Learning Re-search, pages 1371–1382. PMLR.

Boli Chen, Xin Huang, Lin Xiao, Zixin Cai, and Lip-ing Jing. 2020a. Hyperbolic interaction model forhierarchical multi-label classification. Proceedingsof the AAAI Conference on Artificial Intelligence,34(05):7496–7503.

Boli Chen, Xin Huang, Lin Xiao, and Liping Jing.2020b. Hyperbolic capsule networks for multi-labelclassification. In Proceedings of the 58th AnnualMeeting of the Association for Computational Lin-guistics, pages 3115–3124, Online. Association forComputational Linguistics.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, andKristina Toutanova. 2019. BERT: Pre-trainingof Deep Bidirectional Transformers for LanguageUnderstanding. arXiv:1810.04805 [cs]. ArXiv:1810.04805.

Cunxiao Du, Zhaozheng Chen, Fuli Feng, Lei Zhu,Tian Gan, and Liqiang Nie. 2019. Explicit interac-tion model towards text classification. Proceedingsof the AAAI Conference on Artificial Intelligence,33(01):6359–6366.

Diederik P. Kingma and Jimmy Ba. 2015. Adam: Amethod for stochastic optimization. In 3rd Inter-national Conference on Learning Representations,ICLR 2015, San Diego, CA, USA, May 7-9, 2015,Conference Track Proceedings.

Xin Li and Dan Roth. 2002. Learning question clas-sifiers. In COLING 2002: The 19th InternationalConference on Computational Linguistics.

Xien Liu, Song Wang, Xiao Zhang, Xinxin You,Ji Wu, and Dejing Dou. 2020. Label-guidedLearning for Text Classification. arXiv preprintarXiv:2002.10772.

Shervin Malmasi, Surya Kallumadi, Nicola Ueffing,Oleg Rokhlenko, Eugene Agichtein, and Ido Guy,editors. 2020. Proceedings of The 3rd Workshopon e-Commerce and NLP. Association for Compu-tational Linguistics, Seattle, WA, USA.

Tomas Mikolov, Kai Chen, Greg Corrado, and Jef-frey Dean. 2013. Efficient estimation of wordrepresentations in vector space. arXiv preprintarXiv:1301.3781.

Maximillian Nickel and Douwe Kiela. 2017. Poincaréembeddings for learning hierarchical representa-tions. In Advances in Neural Information Process-ing Systems, volume 30. Curran Associates, Inc.

Bo Pang and Lillian Lee. 2004. A sentimental edu-cation: Sentiment analysis using subjectivity sum-marization based on minimum cuts. In Proceed-ings of the 42nd Annual Meeting of the Associationfor Computational Linguistics (ACL-04), pages 271–278, Barcelona, Spain.

Jeffrey Pennington, Richard Socher, and ChristopherManning. 2014. GloVe: Global vectors for wordrepresentation. In Proceedings of the 2014 Confer-ence on Empirical Methods in Natural LanguageProcessing (EMNLP), pages 1532–1543, Doha,Qatar. Association for Computational Linguistics.

Richard Socher, Alex Perelygin, Jean Wu, JasonChuang, Christopher D. Manning, Andrew Ng, andChristopher Potts. 2013. Recursive deep modelsfor semantic compositionality over a sentiment tree-bank. In Proceedings of the 2013 Conference onEmpirical Methods in Natural Language Processing,pages 1631–1642, Seattle, Washington, USA. Asso-ciation for Computational Linguistics.

Ashish Vaswani, Noam Shazeer, Niki Parmar, JakobUszkoreit, Llion Jones, Aidan N. Gomez, ŁukaszKaiser, and Illia Polosukhin. 2017. Attention is allyou need. In Proceedings of the 31st InternationalConference on Neural Information Processing Sys-tems, NIPS’17, page 6000–6010, Red Hook, NY,USA. Curran Associates Inc.

Guoyin Wang, Chunyuan Li, Wenlin Wang, YizheZhang, Dinghan Shen, Xinyuan Zhang, RicardoHenao, and Lawrence Carin. 2018. Joint embed-ding of words and labels for text classification. InProceedings of the 56th Annual Meeting of the As-sociation for Computational Linguistics (Volume 1:Long Papers), pages 2321–2331, Melbourne, Aus-tralia. Association for Computational Linguistics.

Yandi Xia, Aaron Levine, Pradipto Das, GiuseppeDi Fabbrizio, Keiji Shinzato, and Ankur Datta. 2017.Large-scale categorization of Japanese product titlesusing neural attention models. In Proceedings of the15th Conference of the European Chapter of the As-sociation for Computational Linguistics: Volume 2,Short Papers, pages 663–668, Valencia, Spain. As-sociation for Computational Linguistics.

Lin Xiao, Xin Huang, Boli Chen, and Liping Jing.2019. Label-specific document representation formulti-label text classification. In Proceedings ofthe 2019 Conference on Empirical Methods in Natu-ral Language Processing and the 9th InternationalJoint Conference on Natural Language Process-ing (EMNLP-IJCNLP), pages 466–475, Hong Kong,China. Association for Computational Linguistics.

Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai,Hiroshi Mamitsuka, and Shanfeng Zhu. 2019. At-tentionxml: Label tree-based attention-aware deepmodel for high-performance extreme multi-labeltext classification. In Advances in Neural Informa-tion Processing Systems, volume 32, pages 5820–5830. Curran Associates, Inc.

Huasha Zhao, Parikshit Sondhi, Nguyen Bach, SanjikaHewavitharana, Yifan He, Luo Si, and Heng Ji, ed-itors. 2020. Proceedings of Workshop on NaturalLanguage Processing in E-Commerce. Associationfor Computational Linguistics, Barcelona, Spain.