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Multilingual Semantic Role Labeling with Unified Labels POLYGLOT: Multilingual Semantic Role Labeling with Unified Labels Alan Akbik Yunyao Li IBM Research Almaden Research Center 650 Harry Road, San Jose, CA 95120, USA fakbika,[email protected] Abstract Semantic role labeling (SRL) identifies the predicate-argument structure in text with semantic labels. It plays a key role in un- derstanding natural language. In this pa- per, we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 differ- Figure 1: Example of POLYGLOT predicting English Prop- ent languages from 4 different language Bank labels for a simple German sentence: The verb “kaufen” is correctly identified to evoke the BUY.01 frame, while “ich” groups. The core of POLYGLOT are SRL (I) is recognized as the buyer,“ein neues Auto”(a new car) models for individual languages trained as the thing bought, and “dir”(for you) as the benefactive. with automatically generated Proposition Banks (Akbik et al., 2015). The key fea- Not surprisingly, enabling SRL for languages ture of the system is that it treats the other than English has received increasing atten- semantic labels of the English Proposi- tion. One relevant key effort is to create Proposi- tion Bank as “universal semantic labels”: tion Bank-style resources for different languages, Given a sentence in any of the supported such as Chinese (Xue and Palmer, 2005) and languages, POLYGLOT applies the corre- Hindi (Bhatt et al., 2009). However, the conven- sponding SRL and predicts English Prop- tional approach of manually generating such re- Bank frame and role annotation. The re- sources is costly in terms of time and experts re- sults are then visualized to facilitate the quired, hindering the expansion of SRL to new tar- understanding of multilingual SRL with get languages. this unified semantic representation. An alternative approach is annotation projec- tion (Pado´ and Lapata, 2009; Van der Plas et 1 Introduction al., 2011) that utilizes parallel corpora to trans- Semantic role labeling (SRL) is the task of la- fer predicted SRL labels from English sentences beling predicate-argument structure in sentences onto sentences in a target language. It has shown with shallow semantic information. One promi- great promise in automatically generating such re- nent labeling scheme for the English language is sources for arbitrary target languages. In previ- the Proposition Bank (Palmer et al., 2005) which ous work, we presented an approach based on fil- annotates predicates with frame labels and argu- tered projection and bootstrapped learning to auto- ments with role labels. Role labels roughly con- generate Proposition Bank-style resources for 7 form to simple questions (who, what, when, where, languages, namely Arabic, Chinese, French, Ger- how much, with whom) with regards to the predi- man, Hindi, Russian and Spanish (Akbik et al., cate. SRL is important for understanding natural 2015). language; it has been found useful for many ap- Unified semantic labels across all languages. plications such as information extraction (Fader et One key difference between auto-generated al., 2011) and question answering (Shen and Lap- PropBanks and manually created ones is that the ata, 2007; Maqsud et al., 2014). former use English Proposition Bank labels for Figure 2: Side by side view of English, Chinese and Spanish sentences parsed in POLYGLOT’s Web UI. English PropBank frame and role labels are predicted for all languages: All example sentences evoke the BUY.01 frame and have constituents accordingly labeled with roles such as the buyer, the thing bought, the price paid and the benefactive. all target languages, while the latter use language- experiment with the tool to understand the breadth specific labels. As such, the auto-generated Prop- of shallow semantic concepts currently covered, Banks allow us to train an SRL system to consume and to discuss limitations and the potential of such text in various languages and make predictions in an approach for downstream applications. a shared semantic label set, namely English Prop- Bank labels. Refer to Figures 1 and 2 for examples 2 System Overview of how our system predicts frame and role labels Figure 3 depicts the overall architecture of POLY- from the English Proposition Bank for sentences GLOT. First, we automatically generate labeled in German, English, Chinese and Spanish. training data for each target language with anno- Similar to how Stanford dependencies are one tation projection (Akbik et al., 2015) (Figure 3 basis of universal dependencies (De Marneffe et Step 1). We use the labeled data to train for each al., 2014; Nivre, 2015), we believe that English language an SRL system (Figure 3 Step 2) that PropBank labels have the potential to eventu- predicts English PropBank frame and role labels. ally become a basis of “universal” shallow se- Both the creation of the training data and the train- mantic labels. Such a unified representation of ing of the SRL instances are one-time processes. shallow semantics, we argue, may facilitate ap- POLYGLOT provides a Web-based GUI to al- plications such as multilingual information ex- low users to interact with the SRL systems (Fig- traction and question answering, much in the ure 3 Step 3). Given a natural language sentence, same way that universal dependencies facilitate depending on the language of the input sentence, tasks such as crosslingual learning and the devel- POLYGLOT selects the appropriate SRL instance opment and evaluation of multilingual syntactic and displays on the GUI the semantic parse, as parsers (Nivre, 2015). The key questions, how- well as syntactic information. ever, are (1) to what degree English PropBank frame and role labels are appropriate for different target languages; and (2) how far this approach can handle language-specific phenomena or semantic concepts. Contributions. To facilitate the discussions of the above questions, we present POLYGLOT, an SRL system trained on auto-generated PropBanks for 8 languages plus English, namely Arabic, Chi- nese, French, German, Hindi, Japanese, Russian and Spanish. Given a sentence in one of these 9 languages, the system applies the correspond- ing SRL and visualizes the shallow semantic parse with predicted English PropBank labels. POLY- GLOT allows us to illustrate our envisioned ap- proach of parsing different languages into a shared shallow semantic abstraction based on the English Proposition Bank. It also enables researchers to Figure 3: System overview AM-TMP A0 buy.01 A4 A1 Yesterday, Diego bought his girlfriend some flowers pings. In (Akbik et al., 2015), we additionally proposed a process of filtered projection and boot- strapped learning, and successfully created Propo- sition Banks for 7 target languages. We found the quality of the generated PropBanks to be moderate Ayer, Diego compró flores para su novia to high, depending on the target language. Table 1 AM-TMP A0 buy.01 A1 A4 shows estimated precision, recall and F1-score for Figure 4: Annotation projection for a word-aligned English- Spanish sentence pair. each language with two evaluation methods. Par- tial evaluation counts correctly labeled incomplete In the next sections, we briefly describe the cre- constituents as true positives while exact evalua- ation of the labeled data and the training of the tion only counts correctly labeled complete con- SRL systems, followed by a tour of the Web UI. stituents as true positives. For more details we re- fer the reader to (Akbik et al., 2015) . 3 Auto-Generation of Labeled Data 4 Semantic Role Labeling We followed an annotation projection approach to automatically generate the labeled data for dif- Using the auto-generated labeled data, we ferent languages. This approach takes as input a train the semantic role labeler of the MATE word-aligned parallel corpus of English sentences toolkit (Bjorkelund¨ et al., 2009), which achieved and their translations in a target language (TL). A state-of-the-art semantic F1-score in the multilin- semantic role labeler then predicts labels for the gual semantic role labeling task of the CoNLL- English sentences. In a projection step, these la- 2009 shared task (Hajicˇ et al., 2009). The parser is bels are transferred along word alignments onto implemented as a sequence of local logistic regres- the target language sentences. The underlying the- sion classifiers for the four steps of predicate iden- ory is that translated sentence pairs share a de- tification, predicate classification, argument iden- gree of semantic similarity, making such projec- tification and argument classification. In addition, tion possible (Pado´ and Lapata, 2009). it implements a global reranker to rerank sets of Figure 4 illustrates an example of annotation local predictions. It uses a standard feature set of projection: Using an SRL system trained with the lexical and syntactic features. English Proposition Bank, the English sentence is Preprocessing. Before SRL, we execute a labeled with the appropriate frame (BUY.01) and pipeline of NLP tools to extract the required lex- role labels: “Diego” as the buyer (A0 in PropBank ical, morphological and syntactic features. To fa- annotation), “some flowers” as the thing bought cilitate reproducability of the presented work, we (A1) and “his girlfriend” as the benefactive (A4). use publicly available open source tools and pre- In addition, “yesterday” is labeled AM-TMP, sig- nifying a temporal context of this frame. These PREDICATE ARGUMENT labels are then projected onto the aligned Spanish LANG. Match P R F1 P R F1 Agr κ words. For instance, “compro´” is word-aligned to part. 0.97 0.89 0.93 0.86 0.69 0.77 0.92 0.87 Arabic “bought” and thus labeled as BUY.01. The pro- exact 0.97 0.89 0.93 0.67 0.63 0.65 0.85 0.77 jection produces a Spanish sentence labeled with part. 0.97 0.88 0.92 0.93 0.83 0.88 0.95 0.91 Chinese English PropBank labels; such data can in turn be exact 0.97 0.88 0.92 0.83 0.81 0.82 0.92 0.86 used to train an SRL system for Spanish.
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