Great Service! Fine-grained Parsing of Implicit Arguments (2021)

Abstract: Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review “Great service!”, the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.

## introduction studies of form and meaning, the dual perspectives of the language sign, can be traced back to modern linguistics since de saussure @xcite . they are relevant to modern nlp, as even current large neural language models cannot intrinsically achieve a human-analogous understanding of natural language @xcite . computational linguists attempt to capture syntactic and semantic features by means of constructing meaning representation frameworks, such as ptg @xcite , eds @xcite , amr @xcite and ucca @xcite . through such frameworks, participant a linker l center c connector n adverbial d process p elaborator e quantifier q function f relator r ground g state s parellel scene h time t deictic generic genre-based type-identifiable non-specific iterated-set in recent years, a few studies have been dedicated to annotating and modelling implicit and underspecified language @xcite @xcite . for example, in the online review "there is no delivery", two of the omitted elements are the business agent and delivered items. however, previous works focus on specific phenomena requiring linguistic and collaborative reasoning, e.g., bridging resolution, numeric fused-heads identification and referential communication. such datasets overexpose models to limited linguistic expressions, without leveraging the complete syntactic and semantic features of the context, regardless of the diversity of implicit roles. @xcite and @xcite present works on fine-grained implicit role typology, incorporating it into the meaning representation frameworks amr and ucca, respectively. they lay a foundation for the interpretation of idiosyncratic behaviours of implicit arguments from a linguistic and cognitive perspective. nevertheless, neither provided a dataset ready for computational studies of such implicit arguments. we take the latter as a starting point, addressing several theoretical inconsistencies, and evaluate its applicability by carefully re-annotating their pilot dataset and providing inter-annotator agreement. our categorisation set, consisting of six implicit role types, is compatible with ucca's semantic notion of scene rather than specific linguistic phenomena. furthermore, as opposed to previous work, it tackles only essential implicit arguments, salient in cognitive processing. we design the first semantic parser, with two different transition systems, that has the ability to parse fine-grained implicit arguments for meaning representations and evaluate its performance on the revisited dataset. to conclude, we reflect on this work's objectives and the challenges to face in future research on implicit arguments 1 . ## revisiting implicit argument refinement universal conceptual cognitive annotation (ucca; @xcite , a typologically-motivated meaning representation framework, has been targeted by several parsing shared tasks @xcite @xcite . it uses directed acyclic graphs (dag) anchored in surface tokens, where labelled edges represent semantic relations. in the foundational layer, these are based on the notion of scenes (states and processes), their participants and modifiers. ucca distinguishes primary edges, corresponding to explicit content, from remote edges, allowing cross-scene relations. additionally, implicit units represent entities of importance to the interpretation of a scene that are not explicitly anchored in the text. several refinement layers have been proposed beyond the foundational layer, adding distinctions important for semantic roles and coreference @xcite . there are two scenes invoked in the sentence. "great service" with "service" as a process, and "awesome prices" with "awesome" as a state. who is serving, who is being served and what is priced are distinct implicit arguments (imp), which require reasoning to resolve. genre-based roles refer to conventional omission in the genre @xcite . the corpus is based on online reviews, where reviewers typically do not mention what is under review. therefore, an implicit argument in each scene is marked as genre-based referring to the reviewee. generic roles denote "people in general" @xcite . in the example, the recipient could be anyone, rather than a specific person. ## fine-grained implicit argument corpus as a pilot annotation experiment, cui and hershcovich (2020) reviewed and refined 116 randomly selected passages from an ucca-annotated dataset of web reviews (ucca ewt; @xcite . the dataset was annotated by only one annotator. with our revisited guidelines, we ask two annotators foot_0 to revisit the original dataset, adding or modifying implicit arguments, and subsequently refining their categories, using uccaapp @xcite . ## two transition systems for parsing implicit arguments we build the first neural parser that supports parsing implicit arguments dynamically in meaning representations, with two different transition systems. we design a transition-based parser, modelled upon @xcite : a stack @xmath0. . . , s 1 , s 0 ) holds processed words. @xmath1, b 1 , . . .) is a buffer containing tokens or nodes to be processed. v is a set of nodes, and e is a set of labelled edges. we denote s 0 as the first element on s and b 0 as the first element on b. given a sentence composed by a sequence of tokens t 1 , t 2 , ..., t n , the parser is initialized to have a root node on s, and all surface tokens in b. the parser will at each step deterministically choose the most probable transition based on its current parsing state. oracle action sequences are generated for training on gold-standard annotations. we propose two transition systems, implicit-eager and implicit-standard, to deal with implicit arguments over the architecture of hit-scir 2019 @xcite , which ranked first in ucca parsing in the mrp 2019 shared task @xcite . the transition system incorporates all nine transitions, namely, left-edge, right-edge, shift, reduce, node, swap, left-remote, right-remote and finish. shift, together with reduce, are standard transitions. shift moves b 0 to s, while reduce pops s 0 from s (when it should not be attached to any element in b). following transition-based constituent parsing, node x creates a new non-terminal node @xcite . such node will be created on the buffer, as a parent of s 0 with an x-labelled edge. left-edge x and right-edge x add an xlabelled primary edge between the first two elements on s. when the first element is the parent of the second element on s, left-edge x is executed; in reverse, right-edge x will be chosen when the second element has the first element as its child. the left/right direction is the same as before transition transition after transition condition stack buffer nodes edges stack buffer nodes edges terminal? swap deals with non-planar graphs (a generalisation of non-projective trees), in other words, discontinuous constituents. it pops the second node on s and adds it to the top of b. finish is the terminal transition, which pops the root node and marks the transition state as terminal. in implicit-eager, we introduce a new transition implicit x adding an implicit node to the buffer and attaching it with a labelled edge in one step. in implicit-standard, we simplify the existing node transition to only create a node without attaching it, with the purpose of treating implicit units like primary ones and generating them dynamically. we elaborate their designs in section 4.1 and section 4.2. ## discussion as is indicated in noticeably, implicit-eager has emitted 12 deictic implicit arguments, accounting for 57.1% of all predictions, 14 and 66.7% if including partial matches as deictic & generic and deictic & genre-based. while implicit-standard has a more uniform distribution over prediction categories. deictic, generic and genre-based are the most predicted ones. moreover, one non-specific implicit argument, despite being wrongly labelled, is also predicted by implicit-standard, while implicit-eager has never predicted labels other than deictic, generic and genre-based, which have significantly more instances in the training set. both implicit parsers have predicted correctly four deictic and three generic & genre-based implicit arguments. besides, implicit-standard managed to predict two more correct genre-based while implicit-eager has never predicted successfully stand-alone genre-based implicit argument. implicit-standard has a remarkably higher labelled precision of 66.7% in deictic than implicit-eager of 33.3%, while the latter has a higher precision of 75% in generic & genre-based than implicit-standard of 50%. lamentably, although this paper focuses on fine-grained implicit participants, there are already some implicit arguments of other categories annotated in the foundational layer, especially process and center. interestingly, in the sentence "fresh and excellent quality," implicit-standard generated two implicit arguments, genre-based and process in the scene "fresh." it means not only that it infers the scene misses a genre-based participant, but also a process, i.e., the main relation that evolves in time. ## related work parsing implicit argument was introduced into nlp by @xcite ; @xcite , but has been coarse-grained and annotated within nombank @xcite , limited to ten nominal predicates. @xcite identified ten relevant linguistic phenomena, ran several parsers and associated their output with target dependencies. @xcite used a rule-based method for identifying implicit arguments, which depends on semantic role labelling and coreference resolution. similarly, @xcite ; @xcite ; @xcite proposed parsing methods for semeval 2010 data, but they are only able to parse implicit arguments on a coarse level as well. @xcite built a narrative cloze model and evaluated it on gerber and chai (2010)'s dataset. ## conclusion implicit arguments are pervasive in the text but have not been well studied from a general perspective in nlp. in this work, we revisited a recently proposed fine-grained implicit argument typology by addressing its current deficiencies. we annotated a corpus based on the revised guidelines and designed an evaluation metric for measuring implicit argument parsing performance, demonstrating the annotation's reliability with a superior inter-annotator agreement comparing to other finegrained implicit argument studies. the dataset will be available to facilitate relevant research. we introduced the first semantic parser, with two different transition systems, that can handle and predict implicit nodes dynamically, and label them with promising accuracy as part of the meaning representations. we evaluated it on the new dataset and found that some types of implicit arguments are harder to parse than others and that a simpler transition system performs better on parsing implicit arguments at the cost of primary parsing. the fine-grained implicit argument task is challenging and calls for further research. in future work, we plan to create a large resource of implicit arguments by automatically extracting them from various linguistic constructions in unlabelled text, use it for pre-training of language models, and evaluate them on our and other datasets to gain more insights into this linguistic phenomenon. a post-processing baseline to find implicit arguments after parsing the whole graph would also be interesting for future investigation.