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Paul Trichelair

Alumni

Publications

How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
Paul Trichelair
Ali Emami
Adam Trischler
Kaheer Suleman
Jackie Cheung
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challeng… (see more)e (WSC) and SWAG. The question we ask in this paper is whether improved performance on these benchmarks represents genuine progress towards common-sense-enabled systems. We make case studies of both benchmarks and design protocols that clarify and qualify the results of previous work by analyzing threats to the validity of previous experimental designs. Our protocols account for several properties prevalent in common-sense benchmarks including size limitations, structural regularities, and variable instance difficulty.
2019-11-01
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (published)
doi.org
arxiv.org
On the Evaluation of Common-Sense Reasoning in Natural Language Understanding
Paul Trichelair
Ali Emami
Jackie Cheung
Adam Trischler
Kaheer Suleman
Fernando Diaz
The NLP and ML communities have long been interested in developing models capable of common-sense reasoning, and recent works have significa… (see more)ntly improved the state of the art on benchmarks like the Winograd Schema Challenge (WSC). Despite these advances, the complexity of tasks designed to test common-sense reasoning remains under-analyzed. In this paper, we make a case study of the Winograd Schema Challenge and, based on two new measures of instance-level complexity, design a protocol that both clarifies and qualifies the results of previous work. Our protocol accounts for the WSC's limited size and variable instance difficulty, properties common to other common-sense benchmarks. Accounting for these properties when assessing model results may prevent unjustified conclusions.
2018-11-05
arXiv.org (preprint)
dblp.uni-trier.de
The Hard-CoRe Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
Ali Emami
Paul Trichelair
Adam Trischler
Kaheer Suleman
Hannes Schulz
Jackie Cheung
We introduce a new benchmark task for coreference resolution, Hard-CoRe, that targets common-sense reasoning and world knowledge. Previous c… (see more)oreference resolution tasks have been overly vulnerable to systems that simply exploit the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of sentences in naturally occurring text. With these limitations in mind, we present a resolution task that is both challenging and realistic. We demonstrate that various coreference systems, whether rule-based, feature-rich, graphical, or neural-based, perform at random or slightly above-random on the task, whereas human performance is very strong with high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context and rely only on the antecedents to make a decision.
2018-11-02
ArXiv (preprint)
arxiv.org
arxiv.org
The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
Ali Emami
Paul Trichelair
Adam Trischler
Kaheer Suleman
Hannes Schulz
Jackie Cheung
We introduce a new benchmark for coreference resolution and NLI, KnowRef, that targets common-sense understanding and world knowledge. Previ… (see more)ous coreference resolution tasks can largely be solved by exploiting the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of naturally occurring text. We present a corpus of over 8,000 annotated text passages with ambiguous pronominal anaphora. These instances are both challenging and realistic. We show that various coreference systems, whether rule-based, feature-rich, or neural, perform significantly worse on the task than humans, who display high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision. We then use problem-specific insights to propose a data-augmentation trick called antecedent switching to alleviate this tendency in models. Finally, we show that antecedent switching yields promising results on other tasks as well: we use it to achieve state-of-the-art results on the GAP coreference task.
2018-11-02
Annual Meeting of the Association for Computational Linguistics (published)
doi.org