Shayan Ali Akbar, Md Mosharaf Hossain, Tess Wood, Si-Chi Chin, Erica M Salinas, Victor Alvarez, Erwin Cornejo. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Negation poses a challenge in many natural language understanding tasks.Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we show that doing so benefits models for three natural language understanding tasks.We present an automated procedure to collect pairs of sentences with negation and their affirmative interpretations, resulting in over 150,000 pairs.Experimental results show that leveraging these pairs helps (a) T5 generate affirmative interpretations from negations in a previous benchmark, and (b) a RoBERTa-based classifier solve the task of natural language inference.We also leverage our pairs to build a plug-and-play neural generator that given a negated statement generates an affirmative interpretation.Then, we incorporate the pretrained generator into a RoBERTa-based classifier for sentiment analysis and show that doing so improves the results.Crucially, our proposal does not require any manual effort.* Work was done prior to joining Amazon. English-Norwegian (en-no) parallel sentences: (en)There is no more than one Truth.(no) Og det finnes kun en Sannhet.Backtranslation: And there is only one truth.English-Spanish (en-es) parallel sentences: (en) The term gained traction only after 1999.(es) El término no se popularizó hasta después del 1999.

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances.In this paper, we experiment with neural networks to predict the focus of negation.Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network.Experimental results show that doing so obtains the best results to date.Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.

Negation is underrepresented in existing natural language inference benchmarks.Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments.In this paper, we present a new benchmark for natural language inference in which negation plays an important role.We also show that state-of-the-art transformers struggle making inference judgments with the new pairs. Original pairNew pair w/ negation RTE T: Tropical Storm Debby is blamed for several deaths across the Caribbean.T neg : Tropical Storm Debby is not blamed for several deaths across the Caribbean.H: A tropical storm has caused loss of life.H neg : A tropical storm has not caused loss of life.

Efficient and scalable matrix operations are being highly demanding in the recent era of Machine Learning, Deep Learning, and Big Data Analytics. The two commonly used matrix-matrix operations in the Basic Linear Algebra Subprograms (BLAS) specification are General Matrix-Matrix multiplication (GEMM) and Symmetric Rank-k update (SYRK). The SYRK routine is a specialization of the GEMM routine, where half of the multiplications are skipped as the resultant matrix is known to be symmetric. Fortunately, several linear algebra libraries implement these BLAS routines quite efficiently. The libraries usually partition the input matrices into blocks and place them in processor caches, thus improving performance by leveraging the caches. However, the contemporary libraries are highly optimized for squarish matrices, but the performance degrades significantly for the matrices with edge case (strictly thin or strictly fat shapes) in the multicore machine. The primary reason is that the current state-of-the-art libraries make fixed block shapes based on a processor architecture, and do not consider the shape of the input matrices. In this paper, we propose a new blocking approach, we name it Flexible-blocking, to mitigate the scalability issues. In contrast to the contemporary libraries, our approach formulates the blocks of the input matrices based on the shapes of the matrices as well as the number of threads used in the implementation. Our proposed technique shows noticeable performance improvement on multicore shared-memory machines for the edge case matrices.