Xiang Lorraine Li


2022

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Proceedings of the 7th Workshop on Representation Learning for NLP
Spandana Gella | He He | Bodhisattwa Prasad Majumder | Burcu Can | Eleonora Giunchiglia | Samuel Cahyawijaya | Sewon Min | Maximilian Mozes | Xiang Lorraine Li | Isabelle Augenstein | Anna Rogers | Kyunghyun Cho | Edward Grefenstette | Laura Rimell | Chris Dyer
Proceedings of the 7th Workshop on Representation Learning for NLP

2021

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Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
Xuelu Chen | Michael Boratko | Muhao Chen | Shib Sankar Dasgupta | Xiang Lorraine Li | Andrew McCallum
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existing embedding methods only model triple-level uncertainty, and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle) and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to its probabilistic calibration and ability to capture high-order dependencies among facts.

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Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization
Anshuman Mishra | Dhruvesh Patel | Aparna Vijayakumar | Xiang Lorraine Li | Pavan Kapanipathi | Kartik Talamadupula
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled. In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization (CFCS) tasks to investigate potential reasons for this. Our findings show that: (1) the relatively shorter length of premises in traditional NLI datasets is the primary challenge prohibiting usage in downstream applications (which do better with longer contexts); (2) this challenge can be addressed by automatically converting resource-rich reading comprehension datasets into longer-premise NLI datasets; and (3) models trained on the converted, longer-premise datasets outperform those trained using short-premise traditional NLI datasets on downstream tasks primarily due to the difference in premise lengths.

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Box-To-Box Transformations for Modeling Joint Hierarchies
Shib Sankar Dasgupta | Xiang Lorraine Li | Michael Boratko | Dongxu Zhang | Andrew McCallum
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Learning representations of entities and relations in structured knowledge bases is an active area of research, with much emphasis placed on choosing the appropriate geometry to capture the hierarchical structures exploited in, for example, isa or haspart relations. Box embeddings (Vilnis et al., 2018; Li et al., 2019; Dasgupta et al., 2020), which represent concepts as n-dimensional hyperrectangles, are capable of embedding hierarchies when training on a subset of the transitive closure. In Patel et al., (2020), the authors demonstrate that only the transitive reduction is required and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes. While it is possible to represent joint hierarchies with this method, the parameters for each hierarchy are decoupled, making generalization between hierarchies infeasible. In this work, we introduce a learned box-to-box transformation that respects the structure of each hierarchy. We demonstrate that this not only improves the capability of modeling cross-hierarchy compositional edges but is also capable of generalizing from a subset of the transitive reduction.