Unpacking Ambiguous Structure: A Dataset for Ambiguous Implicit Discourse Relations for E nglish and E gyptian A rabic (2023)

Abstract: In this paper, we present principles of constructing and resolving ambiguity in implicit discourse relations. Following these principles, we created a dataset in both English and Egyptian Arabic that controls for semantic disambiguation, enabling the investigation of prosodic features in future work. In these datasets, examples are two-part sentences with an implicit discourse relation that can be ambiguously read as either causal or concessive, paired with two different preceding context sentences forcing either the causal or the concessive reading. We also validated both datasets by humans and language models (LMs) to study whether context can help humans or LMs resolve ambiguities of implicit relations and identify the intended relation. As a result, this task posed no difficulty for humans, but proved challenging for BERT/CamelBERT and ELECTRA/AraELECTRA models.

## introduction coherence is essential for effective communication in written or spoken language @xcite , and discourse connectives play a crucial role in achieving it by helping readers or listeners to infer the intended discourse relation holding between two text spans (asr and demberg, 2020). listeners generally have little difficulty recovering the intended meanings with implicit connectives which are inferred between two juxtaposed independent sentences. they evidence this by combining lexical cues, general reasoning, and prosodic cues to effectively identify the implicit discourse relation. when interpreting ambiguous implicit relations, prosodic cues can be used for disambiguation in spoken language @xcite , while semantics is essential in both speech and writing, ensuring effective communication and understanding. consider for instance the following examples: the discourse relations in both (a) and (b) can be understood by listeners and readers because the connectives "so" and "but", respectively, explicitly indicate the discourse relation. although the implicit discourse relation is ambiguous in (c), listeners might be able to infer it through prosody. however, it is still an open question whether specific prosodic cues are helpful for disambiguation in this case. moreover, disambiguation can also be achieved in written and spoken language using semantic cues (e.g., additional context), such as adding different preceding context sentences that can enforce either the causal or the concessive reading. for instance, the preceding context for the casual and concessive reading can be: the additional context can influence the ambiguous structure, suggesting a likely interpretation in (1) that her preference for tall men implies a causal relation, while her preference for short men indicates a concessive interpretation in (2). we observed that the ambiguous structure of implicit relations arises when the first argument (arg1) does not provide specific details about the event being described and can be influenced by additional context information. however, for some ambiguous examples or structures, there is a clearly preferred reading even without any context, unless there is extremely strong evidence for a different reading. consider for instance the following example (adapted from @xcite ) (a) max fell. john pushed him. the preferred reading for this example is that the pushing caused the falling. however, there is another possible reading where max fell first and was later pushed by john, but it needs extremely strong evidence to force this interpretation. this means that it is hard to figure out if other aspects than semantics contribute to inferring the intended reading. in order to explore how ambiguous implicit relations can be successfully resolved by the listener, we plan to conduct, in future work, a controlled experiment on the impact of prosody without being disambiguated by the semantic component. to support this, this study presents a small dataset of "truly" ambiguous examples for implicit discourse relations for both english and egyptian arabic, which cannot be resolved in the absence of any context, so that it enables a future investigation of prosodic features. we create a set of sentences with an implicit discourse relation that can be ambiguously read as either causal or concessive with two different preceding context sentences forcing either the causal or the concessive reading. the dataset is validated by humans who read these sentences and filled in the intended implicit discourse connective by choosing the most appropriate option from the provided list of connectives. we were able to identify the ambiguous structure of implicit discourse relations and propose a new set of principles to construct ambiguity in implicit discourse relations. this process led to the creation of a small dataset for english and egyptian arabic that was validated by human participants. as far as we are aware, this is the first dataset that addresses ambiguous implicit discourse relations. since human participants were able to identify the intended implicit connectives in a set of examples, we investigate whether language models like bert @xcite and electra @xcite can also fill in the implicit connectives in the examples correctly, which is a challenging task, as context barely influenced the choice made by these models. ## ambiguity in inferring implicit relations each discourse relation involves two arguments, which are typically expressed as two clauses or phrases @xcite . @xcite argues that underspecified representations must be ambiguous. drawing from this notion, we have shaped our own study's approach to examining the first argument with ambiguity in mind. the results of the validation confirm that if argument 1 does not provide information that is relevant to inferring a specific discourse relation, it is not possible to make an inference about that relation unless there are underlying assumptions or presuppositions. in this case, it may be necessary to look for additional information from context to infer the implicit discourse relation. the meaning of argument 1 can be shaped and influenced by context if it carries a neutral connotation, and argument 2 gives additional information or detail based on the event influenced by context. consider the example in figure 1 , where argument 1 "john is tall" in both sentences is unspecified and needs to be interpreted in the context of the sentence to determine the intended information conveyed by argument 2 "she will ask him out". in the first sentence, the context helped argument 1 convey a positive meaning to infer that she has a preference for tall men, and because john fits this preference, she will ask him out, while in the second sentence, the context helped argument 1 convey a negative meaning to infer that she does not have a preference for tall men, but she will still ask john out, even though he fits this preference. while there is a lot of evidence that the context can disambiguate the discourse relation structure @xcite , we still do not have a thorough understanding of how ambiguity in implicit relations is structured, and how they can be interpreted only by context. in this regard, this study examines whether different preceding context influence whether the causal or concessive reading is elicited. a dataset was created and validated to investigate this question in two languages (english and egyptian arabic). ## constructing data for ambiguous implicit discourse relations creating a dataset for implicit discourse relations that involve ambiguity can be a challenging task. this is because the ambiguous structure is not linguistically defined in a way that influences meaning. furthermore, inferring implicit discourse relations can be difficult, since it requires a nuanced understanding of language and discourse. therefore, we aim to investigate this gap by identifying the ambiguous structure of implicit discourse relations and proposing a method to build a dataset for inferring relations by context. ## pilot experiment since human participants were able to identify the intended implicit connectives in a set of examples, we now investigate whether language models like bert and electra will also be able to correctly fill in the implicit connectives within the provided examples. ## conclusion and future work in this paper, we introduced principles of constructing and inferring ambiguity in implicit discourse relations, and created a dataset for ambiguous implicit discourse relations, specifically causal and concessive relations for both english and egyptian arabic. we also validated both datasets by humans and language models (lms) to study whether context can help humans or lms resolve ambiguities of implicit relations and identify the intended relation. for future work, we plan to conduct a controlled experiment on the impact of prosody to figure out whether specific prosodic features correlate with the disambiguation of implicit discourse relations. we also intend to construct more examples to build a classification model to identify the two implicit discourse relations.