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Causes of Poor Performance in Explicit-to-Implicit Discourse Relation Recognition


Core Concepts
One cause for the poor transfer performance from explicit to implicit discourse relations is the occurrence of label shift when deleting connectives from explicit examples.
Abstract
The paper investigates the causes for the poor performance of classifiers trained on explicit discourse relation examples when applied to real implicit scenarios. The key findings are: Manual and empirical analyses show that removing connectives from explicit examples can lead to a change in the discourse relations expressed, a phenomenon called "label shift". This is because connectives play an important role in signaling the discourse relations. The authors devise a metric to quantify the degree of label shift in each explicit example. They find that around 33% of explicit examples in PDTB 2.0 and 29.6% in PDTB 3.0 have a substantial label shift. The authors analyze four factors that contribute to the label shift: the syntactic role of the connective, the ambiguity of the connective, the status of the arguments (intra- or inter-sentential), and the length of the input. They find that the syntactic role of the connective is the most influential factor. To mitigate the impact of label shift, the authors propose two strategies: (1) filtering out explicit examples with high label shift, and (2) joint learning to recover the discarded connective during training. Experiments on PDTB 2.0, PDTB 3.0, and the GUM dataset show that these strategies can effectively improve the performance of explicit-to-implicit discourse relation recognition.
Stats
Around 33% of explicit examples in PDTB 2.0 and 29.6% in PDTB 3.0 have a cosine similarity of less than 0.5, suggesting a substantial label shift. The syntactic role played by connectives has the largest correlation coefficient with the label shift metric, indicating it is the most influential factor.
Quotes
"We show that one cause for such failure is a label shift after connectives are eliminated. Specifically, we find that the discourse relations expressed by some explicit instances will change when connectives disappear." "Referring to example (3), which contains the connective then and is annotated as a Temporal.Asynchronous relation: [Crossland Savings Bank's stock plummeted.]Arg1 Then [management recommended a suspension of dividend payments on both its common and preferred stock.]Arg2 When the connective then is removed, the example, however, tends to express a Contingency.Cause relation because the first argument describes a result of "stock plummet" and the second argument gives the reason, a suspension of dividend pay."

Deeper Inquiries

How do the findings in this paper generalize to other discourse relation frameworks beyond PDTB and RST, such as Segmented Discourse Representation Theory (SDRT)?

The findings in this paper regarding label shift when removing connectives from explicit discourse relations can potentially generalize to other discourse relation frameworks like Segmented Discourse Representation Theory (SDRT). SDRT, like PDTB and RST, involves the analysis of discourse relations between text segments. Therefore, the phenomenon of label shift due to the removal of connectives may also occur in SDRT annotated corpora. However, the specific characteristics and annotation guidelines of SDRT may introduce variations in the extent and nature of label shift compared to PDTB and RST. Further research and experimentation would be needed to confirm the generalizability of the findings to SDRT and other discourse relation frameworks.

What other factors, beyond the ones considered in this paper, could contribute to the label shift phenomenon when removing connectives from explicit discourse relations?

In addition to the factors explored in the paper, several other factors could contribute to the label shift phenomenon when removing connectives from explicit discourse relations: Lexical Ambiguity: The presence of ambiguous terms or phrases in the arguments themselves could lead to different interpretations and hence label shift when connectives are removed. Pragmatic Inferences: Pragmatic aspects of language use, such as implicatures and presuppositions, could influence the perceived discourse relations and contribute to label shift. Discourse Coherence: The overall coherence and cohesion of the discourse, including the presence of anaphoric references or discourse markers beyond connectives, could impact the inferred relations. Syntactic Complexity: The syntactic complexity of the arguments, including the presence of subordination or coordination structures, could affect the interpretation of relations in the absence of connectives. World Knowledge: The reliance on external knowledge or context to infer relations could introduce variability in the label shift phenomenon. Exploring these additional factors in future studies could provide a more comprehensive understanding of the complexities involved in discourse relation recognition and label shift.

Given the importance of connectives in discourse relation recognition, how can we leverage connective information more effectively in neural models beyond the joint learning approach proposed in this paper?

To leverage connective information more effectively in neural models for discourse relation recognition, several strategies can be considered beyond the joint learning approach: Feature Engineering: Incorporate specific features related to connectives, such as syntactic properties, semantic cues, and discourse function, into the model architecture to enhance the representation learning process. Attention Mechanisms: Implement attention mechanisms that focus on connective tokens or their surrounding context to capture the importance of connectives in determining discourse relations. Multi-Task Learning: Train neural models on tasks that involve connective prediction or connective-aware relation classification simultaneously to encourage the model to learn robust representations for connectives and their impact on relations. Transfer Learning: Utilize pre-trained language models fine-tuned on tasks involving connective understanding to improve the model's ability to handle connective information in discourse relation recognition tasks. Ensemble Methods: Combine multiple neural models that specialize in different aspects of connective processing, such as connective detection, connective sense disambiguation, and relation classification, to create a more comprehensive and accurate system. By exploring these strategies and potentially combining them with the joint learning approach, neural models can better leverage connective information for more effective discourse relation recognition.
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