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Found 24 papers, 11 papers with code
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
TableFormer: Robust Transformer Modeling for Table-Text Encoding
Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Disfluencies is an under-studied topic in NLP, even though it is ubiquitous in human conversation.
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL.
A General-Purpose Algorithm for Constrained Sequential Inference
We experimentally show the benefits of our algorithm on constituency parsing and semantic role labeling.
Attention Interpretability Across NLP Tasks
The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque.
Combining Discourse Markers and Cross-lingual Embeddings for Synonym--Antonym Classification
It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms (Grefenstette, 1992; Pad{\'o} and Lapata, 2003).
Joint Multilingual Supervision for Cross-lingual Entity Linking
This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language.
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction.
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences.
Robust Cross-lingual Hypernymy Detection using Dependency Context
We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts.
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i. e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
Revisiting the Evaluation for Cross Document Event Coreference
Cross document event coreference (CDEC) is an important task that aims at aggregating event-related information across multiple documents.
Equation Parsing: Mapping Sentences to Grounded Equations
We introduce the problem of Equation Parsing -- given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence.
Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems
We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook.
Cross-lingual Models of Word Embeddings: An Empirical Comparison
Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature.
IllinoisSL: A JAVA Library for Structured Prediction
IllinoisSL is a Java library for learning structured prediction models.
