Jason Weston


2020

pdf bib
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills
Eric Michael Smith | Mary Williamson | Kurt Shuster | Jason Weston | Y-Lan Boureau
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.

pdf bib
Image-Chat: Engaging Grounded Conversations
Kurt Shuster | Samuel Humeau | Antoine Bordes | Jason Weston
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014). In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019). Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215 possible style traits. Automatic metrics and human evaluations of engagingness show the efficacy of our approach; in particular, we obtain state-of-the-art performance on the existing IGC task, and our best performing model is almost on par with humans on the Image-Chat test set (preferred 47.7% of the time).

pdf bib
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
Kurt Shuster | Da Ju | Stephen Roller | Emily Dinan | Y-Lan Boureau | Jason Weston
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-tasking with very large dialogue datasets in a similar domain, and that the multi-tasking in general provides gains to both text and image-based tasks using several metrics in both the fine-tune and task transfer settings. We obtain state-of-the-art results on many of the tasks, providing a strong baseline for this challenge.

pdf bib
Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Margaret Li | Stephen Roller | Ilia Kulikov | Sean Welleck | Y-Lan Boureau | Kyunghyun Cho | Jason Weston
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely too much on copying from the context, (ii) contain repetitions within utterances, (iii) overuse frequent words, and (iv) at a deeper level, contain logical flaws.In this work we show how all of these problems can be addressed by extending the recently introduced unlikelihood loss (Welleck et al., 2019) to these cases. We show that appropriate loss functions which regularize generated outputs to match human distributions are effective for the first three issues. For the last important general issue, we show applying unlikelihood to collected data of what a model should not do is effective for improving logical consistency, potentially paving the way to generative models with greater reasoning ability. We demonstrate the efficacy of our approach across several dialogue tasks.

pdf bib
Adversarial NLI: A New Benchmark for Natural Language Understanding
Yixin Nie | Adina Williams | Emily Dinan | Mohit Bansal | Jason Weston | Douwe Kiela
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new dataset leads to state-of-the-art performance on a variety of popular NLI benchmarks, while posing a more difficult challenge with its new test set. Our analysis sheds light on the shortcomings of current state-of-the-art models, and shows that non-expert annotators are successful at finding their weaknesses. The data collection method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate.

pdf bib
Multi-Dimensional Gender Bias Classification
Emily Dinan | Angela Fan | Ledell Wu | Jason Weston | Douwe Kiela | Adina Williams
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a new, crowdsourced evaluation benchmark. Distinguishing between gender bias along multiple dimensions enables us to train better and more fine-grained gender bias classifiers. We show our classifiers are valuable for a variety of applications, like controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness.

pdf bib
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Emily Dinan | Angela Fan | Adina Williams | Jack Urbanek | Douwe Kiela | Jason Weston
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Social biases present in data are often directly reflected in the predictions of models trained on that data. We analyze gender bias in dialogue data, and examine how this bias is not only replicated, but is also amplified in subsequent generative chit-chat dialogue models. We measure gender bias in six existing dialogue datasets before selecting the most biased one, the multi-player text-based fantasy adventure dataset LIGHT, as a testbed for bias mitigation techniques. We consider three techniques to mitigate gender bias: counterfactual data augmentation, targeted data collection, and bias controlled training. We show that our proposed techniques mitigate gender bias by balancing the genderedness of generated dialogue utterances, and find that they are particularly effective in combination. We evaluate model performance with a variety of quantitative methods—including the quantity of gendered words, a dialogue safety classifier, and human assessments—all of which show that our models generate less gendered, but equally engaging chit-chat responses.

2019

pdf bib
Learning to Speak and Act in a Fantasy Text Adventure Game
Jack Urbanek | Angela Fan | Siddharth Karamcheti | Saachi Jain | Samuel Humeau | Emily Dinan | Tim Rocktäschel | Douwe Kiela | Arthur Szlam | Jason Weston
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We introduce a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.

pdf bib
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
Dongyeop Kang | Anusha Balakrishnan | Pararth Shah | Paul Crook | Y-Lan Boureau | Jason Weston
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone’s preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then finetune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models finetuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.

pdf bib
Finding Generalizable Evidence by Learning to Convince Q&A Models
Ethan Perez | Siddharth Karamcheti | Rob Fergus | Jason Weston | Douwe Kiela | Kyunghyun Cho
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage. Rather than finding evidence that convinces one model alone, we find that agents select evidence that generalizes; agent-chosen evidence increases the plausibility of the supported answer, as judged by other QA models and humans. Given its general nature, this approach improves QA in a robust manner: using agent-selected evidence (i) humans can correctly answer questions with only ~20% of the full passage and (ii) QA models can generalize to longer passages and harder questions.

pdf bib
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
Emily Dinan | Samuel Humeau | Bharath Chintagunta | Jason Weston
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The detection of offensive language in the context of a dialogue has become an increasingly important application of natural language processing. The detection of trolls in public forums (Galán-García et al., 2016), and the deployment of chatbots in the public domain (Wolf et al., 2017) are two examples that show the necessity of guarding against adversarially offensive behavior on the part of humans. In this work, we develop a training scheme for a model to become robust to such human attacks by an iterative build it, break it, fix it scheme with humans and models in the loop. In detailed experiments we show this approach is considerably more robust than previous systems. Further, we show that offensive language used within a conversation critically depends on the dialogue context, and cannot be viewed as a single sentence offensive detection task as in most previous work. Our newly collected tasks and methods are all made open source and publicly available.

pdf bib
ELI5: Long Form Question Answering
Angela Fan | Yacine Jernite | Ethan Perez | David Grangier | Jason Weston | Michael Auli
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum “Explain Like I’m Five” (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline.However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement.

pdf bib
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
Braden Hancock | Antoine Bordes | Pierre-Emmanuel Mazare | Jason Weston
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user’s responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot’s dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.

pdf bib
Dialogue Natural Language Inference
Sean Welleck | Jason Weston | Arthur Szlam | Kyunghyun Cho
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model’s consistency.

pdf bib
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
Ilia Kulikov | Alexander Miller | Kyunghyun Cho | Jason Weston
Proceedings of the 12th International Conference on Natural Language Generation

We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate responses. We evaluate these strategies in realistic full conversations with humans and propose a model-based Bayesian calibration to address annotator bias. These conversations are analyzed using two automatic metrics: log-probabilities assigned by the model and utterance diversity. Our experiments reveal that better search algorithms lead to higher rated conversations. However, finding the optimal selection mechanism to choose from a more diverse set of candidates is still an open question.

pdf bib
What makes a good conversation? How controllable attributes affect human judgments
Abigail See | Stephen Roller | Douwe Kiela | Jason Weston
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

A good conversation requires balance – between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chit-chat dialogue: repetition, specificity, response-relatedness and question-asking. We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task. We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.

2018

pdf bib
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Saizheng Zhang | Emily Dinan | Jack Urbanek | Arthur Szlam | Douwe Kiela | Jason Weston
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i)condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

pdf bib
Jump to better conclusions: SCAN both left and right
Jasmijn Bastings | Marco Baroni | Jason Weston | Kyunghyun Cho | Douwe Kiela
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic use-cases for sequence-to-sequence models.

pdf bib
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Jason Weston | Emily Dinan | Alexander Miller
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it – the final sequence generator treating the retrieval as additional context. We show on the recent ConvAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations.

2017

pdf bib
Reading Wikipedia to Answer Open-Domain Questions
Danqi Chen | Adam Fisch | Jason Weston | Antoine Bordes
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.

pdf bib
Proceedings of the 2nd Workshop on Representation Learning for NLP
Phil Blunsom | Antoine Bordes | Kyunghyun Cho | Shay Cohen | Chris Dyer | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Yih
Proceedings of the 2nd Workshop on Representation Learning for NLP

pdf bib
ParlAI: A Dialog Research Software Platform
Alexander Miller | Will Feng | Dhruv Batra | Antoine Bordes | Adam Fisch | Jiasen Lu | Devi Parikh | Jason Weston
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce ParlAI (pronounced “par-lay”), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others’ models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.

2016

pdf bib
Key-Value Memory Networks for Directly Reading Documents
Alexander Miller | Adam Fisch | Jesse Dodge | Amir-Hossein Karimi | Antoine Bordes | Jason Weston
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Proceedings of the 1st Workshop on Representation Learning for NLP
Phil Blunsom | Kyunghyun Cho | Shay Cohen | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Wen-tau Yih
Proceedings of the 1st Workshop on Representation Learning for NLP

2015

pdf bib
A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush | Sumit Chopra | Jason Weston
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution
Sam Wiseman | Alexander M. Rush | Stuart Shieber | Jason Weston
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

pdf bib
Semantic Frame Identification with Distributed Word Representations
Karl Moritz Hermann | Dipanjan Das | Jason Weston | Kuzman Ganchev
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Question Answering with Subgraph Embeddings
Antoine Bordes | Sumit Chopra | Jason Weston
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

pdf bib
#TagSpace: Semantic Embeddings from Hashtags
Jason Weston | Sumit Chopra | Keith Adams
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

bib
Embedding Methods for Natural Language Processing
Antoine Bordes | Jason Weston
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Embedding-based models are popular tools in Natural Language Processing these days. In this tutorial, our goal is to provide an overview of the main advances in this domain. These methods learn latent representations of words, as well as database entries that can then be used to do semantic search, automatic knowledge base construction, natural language understanding, etc. Our current plan is to split the tutorial into 2 sessions of 90 minutes, with a 30 minutes coffee break in the middle, so that we can cover in a first session the basics of learning embeddings and advanced models in the second session. This is detailed in the following.Part 1: Unsupervised and Supervised EmbeddingsWe introduce models that embed tokens (words, database entries) by representing them as low dimensional embedding vectors. Unsupervised and supervised methods will be discussed, including SVD, Word2Vec, Paragraph Vectors, SSI, Wsabie and others. A comparison between methods will be made in terms of applicability, type of loss function (ranking loss, reconstruction loss, classification loss), regularization, etc. The use of these models in several NLP tasks will be discussed, including question answering, frame identification, knowledge extraction and document retrieval.Part 2: Embeddings for Multi-relational DataThis second part will focus mostly on the construction of embeddings for multi-relational data, that is when tokens can be interconnected in different ways in the data such as in knowledge bases for instance. Several methods based on tensor factorization, collective matrix factorization, stochastic block models or energy-based learning will be presented. The task of link prediction in a knowledge base will be used as an application example. Multiple empirical results on the use of embedding models to align textual information to knowledge bases will also be presented, together with some demos if time permits.

2013

pdf bib
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
Jason Weston | Antoine Bordes | Oksana Yakhnenko | Nicolas Usunier
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2007

pdf bib
Fast Semantic Extraction Using a Novel Neural Network Architecture
Ronan Collobert | Jason Weston
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics