Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns Kellie Webster and Marta Recasens and Vera Axelrod and Jason Baldridge Google AI Language fwebsterk|recasens|vaxelrod|[email protected] Abstract (1) In May, Fujisawa joined Mari Motohashi’s rink as the team’s skip, moving back from Karuizawa to Ki- Coreference resolution is an important task tami where she had spent her junior days. for natural language understanding, and the resolution of ambiguous pronouns a long- With this scope, we make three key contributions: standing challenge. Nonetheless, existing • We design an extensible, language- corpora do not capture ambiguous pronouns in sufficient volume or diversity to accu- independent mechanism for extracting rately indicate the practical utility of mod- challenging ambiguous pronouns from text. els. Furthermore, we find gender bias in • We build and release GAP, a human-labeled existing corpora and systems favoring mas- corpus of 8,908 ambiguous pronoun-name culine entities. To address this, we present pairs derived from Wikipedia.2 This dataset and release GAP, a gender-balanced labeled targets the challenges of resolving naturally- corpus of 8,908 ambiguous pronoun-name occurring ambiguous pronouns and rewards pairs sampled to provide diverse coverage systems which are gender-fair. of challenges posed by real-world text. We explore a range of baselines which demon- • We run four state-of-the-art coreference re- strate the complexity of the challenge, the solvers and several competitive simple base- best achieving just 66.9% F1. We show lines on GAP to understand limitations in that syntactic structure and continuous neu- current modeling, including gender bias. ral models provide promising, complemen- We find that syntactic structure and Trans- tary cues for approaching the challenge. former models (Vaswani et al., 2017) pro- vide promising, complementary cues for ap- 1 Introduction proaching GAP. Coreference resolution decisions can drastically Coreference resolution involves linking referring alter how automatic systems process text. Biases expressions that evoke the same discourse en- in automatic systems have caused a wide range tity, as defined in shared tasks such as CoNLL of underrepresented groups to be served in an in- 2011/12 (Pradhan et al., 2012) and MUC (Gr- equitable way by downstream applications (Hardt, ishman and Sundheim, 1996). Unfortunately, 2014). We take the construction of the new GAP high scores on these tasks do not necessarily corpus as an opportunity to reduce gender bias in translate into acceptable performance for down- arXiv:1810.05201v1 [cs.CL] 11 Oct 2018 coreference datasets; in this way, GAP can pro- stream applications such as machine translation mote equitable modeling of reference phenom- (Guillou, 2012) and fact extraction (Nakayama, ena complementary to the recent work of Zhao 2008). In particular, high-scoring systems suc- et al. (2018) and Rudinger et al. (2018). Such cessfully identify coreference relationships be- approaches promise to improve equity of down- tween string-matching proper names, but fare stream models, such as triple extraction for knowl- worse on anaphoric mentions such as pronouns edge base population. and common noun phrases (Stoyanov et al., 2009; Rahman and Ng, 2012; Durrett and Klein, 2013). We consider the problem of resolving gendered biguous pronoun in bold, the two potential coreferent names ambiguous pronouns in English, such as she1 in: in italics, and the correct one also underlined. 2http://goo.gl/language/gap- 1The examples throughout the paper highlight the am- coreference 2 Background Langlais, 2016b). GAP examples are not strictly Winograd Existing datasets do not capture ambiguous pro- schemas because they have no reference-flipping nouns in sufficient volume or diversity to bench- word. Nonetheless, they contain two person mark systems for practical applications. named entities of the same gender and an am- 2.1 Datasets with Ambiguous Pronouns biguous pronoun that may refer to either (or nei- ther). As such, they represent a similarly diffi- Winograd schemas (Levesque et al., 2012) are cult challenge and require the same inferential ca- closely related to our work as they contain am- pabilities. More importantly, GAP is larger than biguous pronouns. They are pairs of short texts existing Winograd schema datasets and the exam- with an ambiguous pronoun and a special word (in ples are from naturally occurring Wikipedia text. square brackets) that switches its referent: GAP complements OntoNotes by providing an ex- (2) The trophy would not fit in the brown suitcase be- tensive targeted dataset of naturally occurring am- cause it was too [big/small]. biguous pronouns. The Definite Pronoun Resolution Dataset (Rah- 2.2 Modeling Ambiguous Pronouns man and Ng, 2012) comprises 943 Winograd schemas written by undergraduate students and State-of-the-art coreference systems struggle to later extended by Peng et al. (2015). The First resolve ambiguous pronouns that require world Winograd Schema Challenge (Morgenstern et al., knowledge and commonsense reasoning (Durrett 2016) released 60 examples adapted from pub- and Klein, 2013). Past efforts have tried to mine lished literary works (Pronoun Disambiguation semantic preferences and inferential knowledge Problem)3 and 285 manually constructed schemas via predicate-argument statistics mined from cor- (Winograd Schema Challenge)4. More recently, pora (Dagan and Itai, 1990; Yang et al., 2005), Rudinger et al. (2018) and Zhao et al. (2018) semantic roles (Kehler et al., 2004; Ponzetto and have created two Winograd schema-style datasets Strube, 2006), contextual compatibility features containing 720 and 3160 sentences, respectively, (Liao and Grishman, 2010; Bansal and Klein, where each sentence contains a gendered pronoun 2012), and event role sequences (Bean and Riloff, and two occupation (or participant) antecedent 2004; Chambers and Jurafsky, 2008). These usu- candidates that break occupational gender stereo- ally bring small improvements in general corefer- types. Overall, ambiguous pronoun datasets have ence datasets and larger improvements in targeted been limited in size and, most notably, consist only Winograd datasets. of manually constructed examples which do not Rahman and Ng (2012) scored 73.05% preci- necessarily reflect the challenges faced by systems sion on their Winograd dataset after incorporating in the wild. targeted features such as narrative chains, Web- In contrast, the largest and most widely-used based counts, and selectional preferences. Peng coreference corpus, OntoNotes (Pradhan et al., et al. (2015)’s system improved the state of the art 2007), is general purpose. In OntoNotes, simpler to 76.41% by acquiring hsubject, verb, objecti and high-frequency coreference examples (e.g. those hsubject/object, verb, verbi knowledge triples. captured by string matching) greatly outnumber In the First Winograd Schema Challenge (Mor- examples of ambiguous pronouns, which obscures genstern et al., 2016), participants used methods performance results on that key class (Stoyanov ranging from logical axioms and inference to neu- et al., 2009; Rahman and Ng, 2012). Ambiguous ral network architectures enhanced with common- pronouns greatly impact main entity resolution sense knowledge (Liu et al., 2017), but no system in Wikipedia, the focus of Ghaddar and Langlais qualified for the second round. Recently, Trinh (2016a), who use WikiCoref, a corpus of 30 full and Le (2018) have achieved the best results on articles annotated with coreference (Ghaddar and the Pronoun Disambiguation Problem and Wino- grad Schema Challenge datasets, achieving 70% 3 https://cs.nyu.edu/faculty/davise/ and 63.7%, respectively, which are 3% and 11% papers/WinogradSchemas/PDPChallenge2016. xml above Liu et al.’s (2017)’s previous state of the 4https://cs.nyu.edu/faculty/davise/ art. Their model is an ensemble of word-level and papers/WinogradSchemas/WSCollection.xml character-level recurrent language models, which Type Pattern Example FINALPRO (Name, Name, Pronoun) Preckwinkle criticizes Berrios’ nepotism: [. ] County’s ethics rules don’t apply to him. MEDIALPRO (Name, Pronoun, Name) McFerran’s horse farm was named Glen View. After his death in 1885, John E. Green acquired the farm. INITIALPRO (Pronoun, Name, Name) Judging that he is suitable to join the team, Butcher injects Hughie with a specially formulated mix. Table 1: Extraction patterns and example contexts for each. despite not being trained on coreference data, en- ing given that learned NLP systems often reflect code commonsense as part of the more general and even amplify training biases (Bolukbasi et al., language modeling task. It is unclear how these 2016; Caliskan et al., 2017; Zhao et al., 2017). A systems perform on naturally-occurring ambigu- growing body of work defines notions of fairness, ous pronouns. For example, Trinh and Le’s (2018) bias, and equality in data and machine-learned system relies on choosing a candidate from a pre- systems (Pedreshi et al., 2008; Hardt et al., 2016; specified list, and it would need to be extended to Zafar et al., 2017; Skirpan and Gorelick, 2017), handle the case that the pronoun does not corefer and debiasing strategies include expanding and with any given candidate. By releasing GAP, we rebalancing data (Torralba and Efros, 2011; Ryu aim to foster research in this direction, and set sev- et al., 2017; Shankar et al., 2017; Buda, 2017), and eral competitive baselines without using targeted balancing performance