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Found 78 papers, 30 papers with code
Formal Basis of a Language Universal
Abstract Steedman (2020) proposes as a formal universal of natural language grammar that grammatical permutations of the kind that have given rise to transformational rules are limited to a class known to mathematicians and computer scientists as the “separable” permutations.
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing
The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages.
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs
In this paper, we introduce the new task of open-domain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction.
Fine-grained General Entity Typing in German using GermaNet
We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing.
Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer
Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains.
Computing All Quantifier Scopes with CCG
We present a method for computing all quantifer scopes that can be extracted from a single CCG derivation.
Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar
Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model.
Neutralizing Bias in LLM Reasoning using Entailment Graphs
Extensive evaluations show that our framework can significantly reduce hallucinations from attestation bias.
Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction
While Large Language Models (LLMs) have showcased remarkable proficiency in reasoning, there is still a concern about hallucinations and unreliable reasoning issues due to semantic associations and superficial logical chains.
Explicit Inductive Inference using Large Language Models
Large Language Models (LLMs) are reported to hold undesirable attestation bias on inference tasks: when asked to predict if a premise P entails a hypothesis H, instead of considering H's conditional truthfulness entailed by P, LLMs tend to use the out-of-context truth label of H as a fragile proxy.
A Language-agnostic Model of Child Language Acquisition
This work reimplements a recent semantic bootstrapping child-language acquisition model, which was originally designed for English, and trains it to learn a new language: Hebrew.
A Usage-centric Take on Intent Understanding in E-Commerce
In this paper, we focus on predicative user intents as "how a customer uses a product", and pose intent understanding as a natural language reasoning task, independent of product ontologies.
Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets
We benchmark metric performance, assess their incremental performance over successive campaigns, and measure their sensitivity to a range of linguistic phenomena.
Sentence-Incremental Neural Coreference Resolution
We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method.
Sources of Hallucination by Large Language Models on Inference Tasks
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization.
Prosodic features improve sentence segmentation and parsing
Parsing spoken dialogue presents challenges that parsing text does not, including a lack of clear sentence boundaries.
Extrinsic Evaluation of Machine Translation Metrics
We calculate the correlation between the metric's ability to predict a good/bad translation with the success/failure on the final task for the Translate-Test setup.
Modeling structure-building in the brain with CCG parsing and large language models
These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability.
Language Models Are Poor Learners of Directional Inference
We examine LMs' competence of directional predicate entailments by supervised fine-tuning with prompts.
Multi-Document Summarization with Centroid-Based Pretraining
In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary.
Smoothing Entailment Graphs with Language Models
The diversity and Zipfian frequency distribution of natural language predicates in corpora leads to sparsity in Entailment Graphs (EGs) built by Open Relation Extraction (ORE).
Zero-shot Cross-Linguistic Learning of Event Semantics
Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face.
Cross-lingual Inference with A Chinese Entailment Graph
Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples.
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking
In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks.
Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech
We then demonstrate the utility of the compiled corpora through (1) a longitudinal corpus study of the prevalence of different syntactic and semantic phenomena in the CDS, and (2) applying an existing computational model of language acquisition to the two corpora and briefly comparing the results across languages.
Blindness to Modality Helps Entailment Graph Mining
Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population.
Modality and Negation in Event Extraction
Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence.
Incorporating Temporal Information in Entailment Graph Mining
We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities.
Prosodic segmentation for parsing spoken dialogue
We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model).
Multivalent Entailment Graphs for Question Answering
Drawing inferences between open-domain natural language predicates is a necessity for true language understanding.
Learning Negation Scope from Syntactic Structure
We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure.
The Role of Reentrancies in Abstract Meaning Representation Parsing
Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations.
Aspectuality Across Genre: A Distributional Semantics Approach
The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences.
Span-Based LCFRS-2 Parsing
Concretely, by using a grammar formalism to restrict the space of possible trees we can use dynamic programming parsing algorithms for exact search for the most probable tree.
Max-Margin Incremental CCG Parsing
Incremental syntactic parsing has been an active research area both for cognitive scientists trying to model human sentence processing and for NLP researchers attempting to combine incremental parsing with language modelling for ASR and MT.
The role of context in neural pitch accent detection in English
We find that these innovations lead to an improvement from 87. 5% to 88. 7% accuracy on pitch accent detection on American English speech in the Boston University Radio News Corpus, a state-of-the-art result.
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction
Combining two graphs requires merging the nodes which are counterparts of each other.
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.
Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference
This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German.
Duality of Link Prediction and Entailment Graph Induction
The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
Wide-Coverage Neural A* Parsing for Minimalist Grammars
Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky{'}s (1995) Minimalist Program.
CCG Parsing Algorithm with Incremental Tree Rotation
The main obstacle to incremental sentence processing arises from right-branching constituent structures, which are present in the majority of English sentences, as well as optional constituents that adjoin on the right, such as right adjuncts and right conjuncts.
Temporal and Aspectual Entailment
Inferences regarding "Jane's arrival in London" from predications such as "Jane is going to London" or "Jane has gone to London" depend on tense and aspect of the predications.
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Neural NLP systems achieve high scores in the presence of sizable training dataset.
Predicting accuracy on large datasets from smaller pilot data
Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from models trained on a small pilot training dataset.
Character-Level Models versus Morphology in Semantic Role Labeling
Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data.
Learning Typed Entailment Graphs with Global Soft Constraints
We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph.
Universal Semantic Parsing
In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs.
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis.
Transforming Dependency Structures to Logical Forms for Semantic Parsing
In contrast{---}partly due to the lack of a strong type system{---}dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages.
Large-scale Semantic Parsing without Question-Answer Pairs
In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs.
Improved CCG Parsing with Semi-supervised Supertagging
Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal.
Combined Distributional and Logical Semantics
We introduce a new approach to semantics which combines the benefits of distributional and formal logical semantics.
