Papers by Daniela Gerz
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling (D18-1)
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| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
| Approach: | They propose to use a full-vocabulary setup to test the performance of language modeling (LM) on 50 typologically diverse languages. |
| Outcome: | The proposed language modeling task is based on a full vocabulary setup focused on word-level prediction on 50 typologically diverse languages. |
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations (2020.acl-main)
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| Challenge: | Span-ConveRT is a light-weight model for dialog slot-filling . we show consistent gains over a span extractor and a BERT-based model . |
| Approach: | They propose a model for dialog slot-filling which frames the task as a turn-based span extraction task. |
| Outcome: | The proposed model is especially useful for few-shot learning scenarios. |
Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)
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Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models . |
| Approach: | They propose a method which pretrains a retrieval-based model on large general-domain conversational corpora and fine-tunes it for the target dialogue domain. |
| Outcome: | The proposed method is evaluated on five diverse domains, ranging from e-commerce to banking. |
Scoring Lexical Entailment with a Supervised Directional Similarity Network (P18-2)
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| Challenge: | Existing word embeddings that use supervision only improve the embeddable word embeds of words with annotated lexical relations. |
| Approach: | They propose a supervised directional similarity network for learning task-specific transformation functions on top of general-purpose word embeddings. |
| Outcome: | The proposed model outperforms existing models on the HyperLex dataset on a directional graded lexical entailment task by 25%. |
Multilingual and Cross-Lingual Intent Detection from Spoken Data (2021.emnlp-main)
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Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, Ivan Vulić
| Challenge: | a systematic study on multilingual and cross-lingual intent detection from spoken data is presented . current work on intent detection is limited to English, and standard benchmarks exist only in English. |
| Approach: | They present a systematic study on multilingual and cross-lingual intent detection from spoken data. |
| Outcome: | The proposed resource is called MInDS-14, and it provides strong intent detection in most target languages. |
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines (N19-1)
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| Challenge: | Experimental results show that standalone n-gram models lend themselves as natural choices for resource-lean or morphologically rich languages. |
| Approach: | They run experiments on 50 languages covering all morphological language families to compare n-gram models with lstm models. |
| Outcome: | The proposed extension outperforms an lstm language model on 42 languages while its extension which explicitly injects linguistic knowledge outperformed the character-aware neural model on 8 languages. |
Multidirectional Associative Optimization of Function-Specific Word Representations (2020.acl-main)
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| Challenge: | Existing approaches to learning word associations rely on distributional hypothesis and learn a single word vector space based on word co-occurrences in large text corpora. |
| Approach: | They propose to induce a joint function-specific word vector space where vectors of plausible SVO compositions lie close together. |
| Outcome: | The proposed model outperforms existing models on selectional preference and event similarity tasks while reducing the number of parameters by up to 95%. |
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking (D19-3)
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Matthew Henderson, Ivan Vulić, Iñigo Casanueva, Paweł Budzianowski, Daniela Gerz, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | a task-oriented dialogue system is based on task-specific ontologies that constrain slots to specific values . we present a conversational search engine that can be used to search for restaurant reservations . |
| Approach: | They propose a conversational search engine that supports task-oriented dialogue . the polyresponse engine is trained on hundreds of millions of examples extracted from real conversations . |
| Outcome: | The proposed system is available in 8 different languages. |
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models (2021.emnlp-main)
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Ivan Vulić, Pei-Hao Su, Samuel Coope, Daniela Gerz, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Tsung-Hsien Wen
| Challenge: | Existing Transformer-based language models (LMs) are not effective as sentence encoders when used off-the-shelf. |
| Approach: | They propose a method which turns a pretrained LM into a universal conversational encoder and task-specialised sentence encoder. |
| Outcome: | The proposed framework achieves state-of-the-art ID performance across the board with particular gains in the most challenging, few-shot setups. |