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Recombining Frames and Roles in Frame-Semantic Parsing

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Recombining Frames and Roles in Frame-Semantic Parsing Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing Gosse Minnema and Malvina Nissim Center for Language and Cognition University of Groningen, The Netherlands fg.f.minnema, [email protected] Abstract (given a predicate-frame pair, find and label its argu- ments). Some recent systems, such as the LSTM- Frame-semantic parsers traditionally predict based Open-SESAME and (Swayamdipta et al., predicates, frames, and semantic roles in a 2017) or the classical-statistical SEMAFOR (Das fixed order. This paper explores the ‘chicken- et al., 2014), implement the full pipeline, but with a or-egg’ problem of interdependencies between these components theoretically and practically. strong focus specifically on argID. Other models We introduce a flexible BERT-based sequence implement some subset of the components (Tan, labeling architecture that allows for predict- 2007; Hartmann et al., 2017; Yang and Mitchell, ing frames and roles independently from each 2017; Peng et al., 2018), while still implicitly adopt- other or combining them in several ways. ing the pipeline’s philosophy.1 However, little fo- Our results show that our setups can approxi- cus has been given to frame-semantic parsing as mate more complex traditional models’ perfor- an end-to-end task, which entails not only imple- mance, while allowing for a clearer view of the interdependencies between the pipeline’s com- menting the separate components of the pipeline, ponents, and of how frame and role prediction but also looking at their interdependencies. models make different use of BERT’s layers. We highlight such interdependencies from a the- oretical perspective, and investigate them empiri- 1 Introduction cally. Specifically, we propose a BERT-based (De- vlin et al., 2019) sequence labeling system that FrameNet (Baker et al., 2003) is a computational allows for exploring frame and role prediction in- framework implementing the theory of frame se- dependently, sequentially, or jointly. Our results (i) mantics (Fillmore, 2006). At its core is the notion suggest that the traditional pipeline is meaningful of linguistic frames, which are used both for classi- but only one of several viable approaches to end- fying word senses and defining semantic roles. For to-end SRL, (ii) highlight the importance of the example, in (1), “bought” is said to evoke the COM- frameID component, and (iii) show that, despite MERCE BUY frame, and “Chuck”, “some eggs”, their interdependence, frame and role prediction and “yesterday” instantiate its associated roles. need different kinds of linguistic information. (1)C OMMERCE BUY Contributions The main contributions of this pa- [Buyer Chuck ] bought [Goods some eggs ] per are the following: [Time yesterday ] • We identify theoretical and practical challenges In NLP, frame-semantic parsing is the task of in the traditional FrameNet SRL pipeline (§2); automatically analyzing sentences in terms of FrameNet frames and roles. It is a form of se- • We introduce a flexible, BERT-based sequence- mantic role labeling (SRL) which defines semantic labeling architecture, and experiment with pre- roles (called frame elements) relative to frames dicting parts of the pipeline separately (§3); (Gildea and Jurafsky, 2002). Canonically (Baker • We explore four methods for re-composing an et al., 2007), frame-semantic parsing has been split end-to-end system (§4); up into a three-component pipeline: targetID 1Yang and Mitchell(2017) and Peng et al.(2018) learn (find frame-evoking predicates), then frameID frames and arguments jointly, but still need targetID as a (map each predicate to a frame), and lastly argID separate step. 155 Proceedings of the 14th International Conference on Computational Semantics, pages 155–165 June 17–18, 2021. ©2021 Association for Computational Linguistics • Through two evaluation metrics, we empirically The roles-make-the-frame problem In (2-3), show the relative contribution of the single com- given the target (“ran”), the task is to find the cor- ponents and their reciprocal impact (§5-6). rect frame and its corresponding roles. In the tradi- tional pipeline, we would do this by first predicting All of our source code and instructions for how a frame, and then labeling the dependents of “ran” to reproduce the experiments is publicly avail- with roles from this frame. However, the question able at https://gitlab.com/gosseminnema/ is what kinds of patterns a frame-finding model bert-for-framenet. needs to learn in order to be successful. It is clearly not sufficient to learn a one-to-one mapping be- 2 On pipelines, chickens, and eggs tween word forms and frames, not just because of According to Fillmore(2006), an essential feature known ambiguous cases (“Angela runs” vs. “a tear of a frame is that it is “any system of concepts re- runs”), but also because of gaps in FrameNet that lated in such a way that to understand any one of conceal unknown ambiguities, such as in (5). them you have to understand the whole structure To distinguish between “ran” in (2) and (3), a in which it fits.” In particular, linguistic frames are model has to to take into account the sentential systems of semantic roles, possible predicates, and context in some way, which is exactly what LSTM- other semantic information. In this section, we dis- based models or BERT-based models can do. But cuss the relationship between these concepts in the what kind of contextual information exactly do context of frame-semantic parsing and highlight in- we need? SELF MOTION and FLUIDIC MOTION terdependencies between the various components. have a very similar syntax and semantics, the cru- cial difference being the semantic category of the 2.1 Challenges for parsers “mover”. Concretely, this means that in (2-3), we The following artificial examples display some of would benefit from recognizing that “Angela” de- the challenges that frame-semantic parsers face: notes an animate entity while “a tear” denotes a fluid. Doing so would amount to doing partial (2)S ELF MOTION semantic role labeling, since we are looking at [Self mover Angela ] ran [Goal to school ] the predicate’s syntactic arguments and their se- mantic properties, which is exactly the informa- (3)F LUIDIC MOTION tion an argID model needs to tag “Angela” with [Fluid A tear ] ran [Path down my cheek ] “Self mover” and “a tear” with “Fluid”. While it (4)E XPEND RESOURCE is possible to use contextual information without [Agent We ] ran out [Resource of cookies ] knowledge of dependency structure (perhaps sim- (5) ; ple co-occurrence is enough), we hypothesize that His girlfriend ran him home.2 such knowledge would be helpful, and thus, that doing frameID and argID simultaneously, or In each example, the predicate contains “ran”, even predicting frameID after argID. but used in different frames. In (2) and (3), the pred- icate is the verb “run”, but used in two different The frames-make-the-targets problem In the senses (running of a person vs. running of a liquid), literature, targetID has received even less at- corresponding to two different frames. Here, the tention than frameID — all models we are aware main parsing challenge is resolving this ambiguity of use gold targetID inputs — but is crucial to and choosing the correct frame (frameID). By the success of any end-to-end model. Theoretically contrast, in (4), the predicate is “run out”. This speaking, the targetID problem is less interest- complex verb is not ambiguous, so the main chal- ing than frameID: since as almost any content lenge in this sentence would be targetID (i.e. word can evoke a frame, assuming a fully com- identifying that the target consists of the two to- plete FrameNet (containing all possible predicates), kens “ran” and “out”). Similarly, in (5), “run” is doing targetID would amount to a (simplified) used in a sense not listed in FrameNet, so the chal- POS-tagging task where content words are labeled lenge here is to make sure nothing is tagged at all. as “yes”, and (most) function words as “no”. However, in practice, FrameNet is far from com- 2See sense #14 of “run” in https:// www.oxfordlearnersdictionaries.com/ plete, so that doing targetID means identify- definition/english/run_1?q=run ing all wordforms that correspond to some pred- 156 icate evoking a frame present in FrameNet, mak- Input Sequence labels Frame structures ing targetID dependent on frameID.3 For ex- Boris R:Self_mover|R:Sound_source ⟨⟨MAKE_NOISE, ⟨screamed⟩⟩, screamed F:Make_noise {⟨Sound_source, ⟨Boris⟩⟩}⟩ ample, to find the target in (2-3), it would suf- and ⟨⟨SELF_MOTION, ⟨ran⟩⟩, ran F:Self_motion {⟨Self_mover,⟨Boris⟩⟩, fice to lemmatize “ran” to “run”, and check if home R:Direction ⟨Direction, ⟨home⟩⟩}⟩ “run” is listed under any FrameNet frames. But this strategy would fail in (4-5): in those cases, Figure 1: Frame structures and sequence labels (N.B.: color added for illustrative purposes only) ‘ran’ is not the full target, but either only a part of it (4), or not at all (5). In order to predict this, we would need to recognize that “run out” is part that neural models like BERT implicitly learn to of the EXPEND RESOURCE frame, and that “run reproduce the classical NLP pipeline, with differ- someone somewhere” is a different sense of “run” ent layers specializing in specific components of that does not match either FLUIDIC MOTION or the pipeline, and the possibility for later layers to SELF MOTION. Hence, targetID seems to pre- dynamically resolve ambiguities found in earlier suppose (partial) frameID in some cases. layers. For the BERT-based models we propose, we study the relationship between different layers 2.2 Pipelines: NLP vs. SRL and the traditional FrameNet pipeline (cf. §6.2). The type of problem that we identified in this sec- tion is not unique to frame-semantic parsing but 3 Dissecting the pipeline also occurs in the standard NLP pipeline of tok- enization, POS-tagging, lemmatization, etc.
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